{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"./data/day.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 离散特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2243450b0f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axis=plt.subplots(nrows=2,ncols=2)\n",
    "fig.set_size_inches(20, 15)\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "sns.countplot(train['season'],ax=axis[0][0])\n",
    "sns.countplot(train['mnth'],ax=axis[0][1])\n",
    "sns.countplot(train['weathersit'],ax=axis[1][0])\n",
    "sns.countplot(train['weekday'],ax=axis[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 连续特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\93246\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22434607c50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wTKvSyTTNh4GHYfrNpwzD2AHcSnQ63VRrALdhGEeBEuAl4IuzrXxK9eV6Z0tLS8bPXJxLp9OJyzVGT88VCwXjdywLl1nV+/LaTH2fTCzPO9G8MhHchQ7cbj9Hmzo5P+SnezRAtzuIPxRNNmU6bBRkOSjJcbClKoeSnNn3SrDqfan3ZPxYdS7jtVzvfGSa5peAL11lk/pLth8C7klkTCIiiTQeCPHYsWgbuhUVuWyoLrA4opm5bkkJL5/tw+MPcaxjmM21RVaHJJJw0610mo1PA0+apjlZ1WQYRgGwGEgHHgA+DCwBnjIMIzVS1CIi88S5Pg/fevkiPz/uYn/7GK3DgcmEE4AvFKHXE+R0n4+fNA7xYoub8QT0ZRARERGZjhfO9DI6HiTdYeNdG6uw2WxWhzQjBdnprK2KJsr2NA+oqlwWhIS0+DcMIwN4D/CHU+83TXM4lnjymKYZim37HqKrptwMPDvTY6X6cr2zpaUl42euzmWzp48KshJ6DIAsZwanhuHFs35eMvsY9Pgpz8+kIi+L8vxMdi0v5Z0bqsjOmNlyr9Oh9+W1m3ifTFQ4VVTEf3WT3pFxnjzRjfnaucn7yvMyWVLqpLY4h+qiHPyhMANuHwMeP4cvDjHg8XO028u5QT9vW1vJ5rqZjcxZtfyv3pPxY9W5jNdyvSIiktr6R3281jwAwK1GOUU5GRZHNDvXN5RwrH2YDpeX1sEx6kqcVockklCJWlfypthzP3HpA1Maik/c7jEMYwCoTlAsIgtCMBzm2VM9vNY88LpmihcHxrg4EJ0a89tjXXz1yTPcs7WGD++oo6Y4x4pwxQKRSIT9FwZ5/FjX5HukriSHm1eUYVTkvW60sKowG4Bdy0vZ0zzA82d6GfOHeORwO8FwZLIvgYiIiEiiPXGii1AkQlFOOjcsK7U6nFmrKc6hqjCbDpeX15oHlHSSeS9RSaedwGHTNN1T74z1eXoGWGOaZmvsvlqgFDidoFhE5r0Bt4+fH2yjfSi6Okia3cbW+iJuMcppKHXS5/bRO+Kjqc/NMyd7cI0F+M7LLTz4Sgt/dPNS/vT25WSmxb/ySZLHmC/IL490TDbeLMxJ549vWcby8lw6h8evum+a3c6u5WVsrCnkFwfbaepz82hjBzkZjskScREREZFEOdszypnuUQDuXFtJuiORXWISy2azcf3SEn5xqJ2TncMMewMp0wxdZDauOekUmy6XYZpm35S71wMnLrP5EaJT6f7DMIwvAFnAN4AXTdPcd62xiCxEjW0uHm3swBcMYwPuWl/J/33vOvKzLv/Hq9/t46f7Wvnxvov0jPj41xebeeFML1/7wAbWLFYCYT7qGPLy430XGfYGAFhfXcC7N1axvb6YAY9/2s+Tl5XOh3fU8e+vttA25OXnB9vIznCwtCw3UaGLiIjIAhcKR3j8WBcADaVO1izOtziia7euqoAnT3Tj9gXZ1zLAHWvi305BJFnEI0X8daJL8k5VAQxeuqFpmj7gTsBNdNW6p4GzwPviEIfIgnPg/CD/ebANXzBMXlYa99+4hPtvWHLFhBNAaW4mn7ltOa/8+Zv4k1uXYbfBme5R3v3t3fzLi01qaDjPnOka4buvNDPsDZDhsHP35mru2VpDVvrsKtsy0uzct7OesrxMQuEIP957kU6XN85Ri4iIiETtbRmgz+3DBrx9fWXKNQ+/nDSHfbJNwYELg4TCuv6W+WvGlU6madZfcvtjl9nmpqvs3wK8e6bHFZHf19Lv5tGjHQA0lDn5g2215Gamke6ws6d5AP80VhnbVl/Ml96+mgdfaaFreJy/f8rk1XP9/OENS0ibZtlyRpqdnUtLrum1SGLsbRngsaOdRIhOp7tvZz0V+dfezD4nM42PX1/Pd15uYdgb4OcH2/jMm5aRZk/dUncRERFJPl5/iOfP9AKwfUkxlQXZFkcUP1vrinj+TC8ef4jmPjcrKvKsDkkkIRLV00lEEmjQ4+fhfa2EI1CRn8lHrqsjc0rlij8YpmOa1SdZ6Q4+dfNSHjvaycGLQ7zWPED38Dgfuq5uWivcTTSbluQRiUR46mQ3r5zrB6L/Rh/dWUfeVSrgZqowJ4OP7Kjj2y800Tfq47WmAW5aMfer04mIiMj89eLZXryBEJlpdm5bVWF1OHFVmJNBfYmTCwMejra5lHSSeUvD0iIpxhcI8aO9Fxjzh8jJcPCRHfW/l3CajXSHnfdsquKO1dE/5i39Hr7zcjNDY9Pv9yPJIRyJ8OvGzsmE06rKfD6xqyGuCacJiwuz2RGrcnv+TO9kzygRERGRa+Ua87OneQCAXcvLyM2cf/USG2qi/VRPdo1Ma5aCSCpS0kkkhUQiER453E7PiA+7De69rpZiZ0Zcnttms3GLUc4HttbgsNvoHfXx4CstDM2g0bRYKxSO8Mihdg5ciLbU21ZfxIeuqyUjLXEf9W9eVUFuZhr+UJjHj3cl7DgiIiKysDx7uodgOEJeVho3Liu1OpyEWLe4ALstOkvhTPeI1eGIJISSTiIp5Ez3KCc7o3+Q3rmhiobS+K8atrGmkI9fX09Gmh3XWIAHX1XiKRUEw2F+dqCVxjYXADcsLeHdG6uwJ7jZZla6gzvXRldcOdExzLme0YQeT0REROa/rmEvR1qj1zS3raxI6ACalXIy0yan1U1cw4nMN/Pzt1dkHgpOqSRZXp7LtvqihB2roSyXj+1U4ilVBENhHt7XOpmQvMUo423r5m51l401hdSX5ADw2LFOgiGVh4uIiMjs/e5kNxGgLDeTLXWJu+ZNBhtqCgE42zPKmC9ocTQi8aekk0iK2N3Uz6DHj90Gb5+DhEJ9qfN1iSeXejwlnWAozMP7WznTHa0wumN1BXesXjSnywnbbDbeuaEKuw363X4OtQ7N2bFFRERkfmnuc3O2xw3AW9ZU4LDP3TWNFVYtyifDYSccgeOdw1aHIxJ3SjqJpIBhb4AXzD4Arl9aSnkclr2fjksTT9/ffQGPRmCSxqUJp7esruAWo9ySWBYVZLGxJjoS+VrTAOFIxJI4REREJHWFIxGeOtENQF1xDqsq8y2OKPEy0uysXhx9nUfblHSS+UdJJ5EU8LuT3fhDYZyZabxp5dwmFepLnXx0Rx0Ou40+t4+H9lzAFwzNaQzyepdLON1sUcJpwg3LoivZ9bl9nIuNUIqIiIhM1/GOYTpcXgDeunZuK7ettDE2xe7CgIcBt8/iaETiS0knkSR3ccAz2VjwLasryEp3zHkMDWW53LO1BhvQPuTl4X2tBMPq22OVYDj5Ek4AlQXZLC1zArC7ud/iaERERCSVBMNhnjnVA8DqynzqSpwWRzR3lpbl4syIXuPvOz9ocTQi8aWkk0iSe+50LwBVhdlstrCR4tqqAt61sQqAc71uHjnUrilUFgiGo03Dky3hNOGG2JLGTb1uuofHLY5GREREUsX+84OT/UvfsmaR1eHMKYfdxpqqAgCOtKk3pswvSjqJJLFOl5emvug0pdtXVWC3uMR4+5Ji3ry6AoBj7cM8fryLiBJPcybZE04AKyryKM3NBFTtJCIiItMzHgjx/JnoQOvWumLK8jItjmjurVqUB0QHd7VqtMwnaVYHICJX9mpT9Et7eV4mKypyLY4m6pYVZbjHg+xpGWBP8wBVBdncMsd9phaiQCjMT5NwSt2l7DYbNywr4dHGThrbXNyxuoK8rHSrwxIREbkme5oH8AcT21ogI83OzqUlCT1Gsnr5XB9j/hDpDhtvWpV81zdzoaEsl3SHjUAowgtmL+/dXG11SCJxoaSTSJJyjfk51h7t5bRreWnSNFK02Wy8fX0lHn+QY+3DPHK4nS31RXxwe63Voc1b/mCYH++7SFNvbPngJE04TdhUU8TTJ3vwBkLsOz/I7asqrA5JRETkmviD4ckG14lSVZid0OdPVsPeALtjA603Lisjf4EOVqU77Cwry+V09yjPnVbSSeYPTa8TSVJ7mgcIRyAvM40N1YVWh/N77DYb79tSzfLyaPXVX/zq+OTythJfvkCIH7x2YTLh9La1i5I64QTRkdrrlhQD0WaYobCmYIqIiMjlPXOqh0AogjPDwa7lpVaHY6mVlfkAvHS2L+GVdSJzRUknkSQ0Hgix/0J05YqdS0tIcyTfr2qa3c6919XSUOokHIHP/uwIx7sTOwK40PiCYf5j93kuDHgAeOeGxdy4vMziqKZneyzp5PEFaY71JRMRERGZqtPl5UhrtHH27Rat0pxMjFhfJ7cvyH6tYifzRPJ9kxURDlwYxBcMk+GwT355T0aZaQ6+8OYVNJQ68QfD/PVzXVwY8lkd1rzgDYT5r5PDtA15sQF3b65mR0Pq9HkozMmgviQHgKNtLoujERERkWQTiUR44kQXEaL9S7fWJe8171zJz0pnSakTgOfO9FgcjUh8KOkkkmRC4QivNQ8AsKW+iJyM5G69lpeVzkP3b6csLxO3P8xfPN2Z8J4H893oeID/OuGizxPEboMPbKthS12R1WHN2PrYtNBTXSMqERcREZHfc6Z7lJa+aDX3nWsrcdiTo3+p1TbVRK+fnjvdq1WiZV5Q0kkkyZzuGmHYG8AG3LA0Nea11xTn8NDHt5OTbqd/LMR9/7Ef15iWep2NAbeP777cwoA3hMMG926vTbqeXtO1rqoAuy06TfBou6qdREREriY/a+YDjU6nE6fTmYBoEisUjvBkrB/osvLcpFmlORlsjCWdWgfHJnt6iqSy5C6hEFmAJua1L6/IpdiZYXE007d6cT5/ddsivvR0J029bu7/wQF+8sAOsjMW9tz8mWjpc/OVJ08z4PHjsMNdRgGrFxdYHdasOTPTWFaey9keN3tbBqwOR0REJKmlO+zsaR6YUXWwyzUGQLOnb9r7ZKTZ2bnU2in7+y8M0u/2YQPuXLsoaVZpTga1xTlUFmTRNTzOs6d7WV6RZ3VIItdESSeRJOL2BTF7RgHYXJt606k2VubwP2+q4Ksv9XC41cVnfnqYf/vwlqRshJ5sjrQOcf8PDjA0FiDDYeeulXnUFKRO0vFKNlQXcrbHzdF2FyPjgQW7DLKIiMh0+IPhGbUp6OmJVhJXkDXtfaoKs2ccVzy5fUGePRXtV7SlrojKAmvjSTY2m403rSznJ/taee50D//jlqVWhyRyTfRNUCSJHG1zEY5AVrqdVbElU1PNzUvy+Ku7VgPw7OlevvTrE5qP/gaeO93DBx/cy9BYgLysNB7YtWReJJwAVlfmk2a3EQhFePqkGmKKiIgsdE+d6MYbCJGVbueONYusDicp3b6qAoDDrUMMetSyQlKbkk4iSeRwbGrd+upC0lO4OuhjNyzh07FRmZ8daOMfnzlrcUTJ66f7W/nkjw4xHghTW5zDX759NdVFOVaHFTeZ6Q5WxhKojzZ2WByNiIhI4kUiEfzBMKGwBt0udb7fM3m9+5Y1i8jN1MSby9m5tITMNDvhCLzW3G91OCLXRL/lIkmi0+Wla3gcSM2pdZf6n28x6B318cihdr75fBMF2ek8sKvB6rCSRigc4f976gzffbkFgLVV+Xz/Y9s51Tky71b/21BdwImOYV5rHqBv1EdZXqbVIYmIiFyzcCRCl2uc8wMeLvR76Bv1MRYIMe4PEYpVeWem2cnJcJCbmUZFfhaVBVlUFmRTWZhFZtrC6nsZCkf4zdHoAFR1UTbb6ostjih5ZaU72FpfxO6mAXY3DXDX+sVWhyQya0o6iSSJiQbipbmZ1BSl/tx2m83GV9+7DtdYgGdP9/B3j58mLyuNe7bVWh2a5cb8QT73s0aeifUzeNPKcr7xwU3zdrRvRUUeORkOxvwhnjjexX3X11sdkoiIyKwNevzsburnSNsQ44GrN/32BcP4gmGGxgK0Df33oJLdBrXFTlZU5LK8PI/Kwun3ZEpVrzX30zMSbR7+rg1V2NU8/KpuWFbK7qYBVTpJypuf33BEUkwoHKGxLdoIcnNt4bxZwSPdYedb927i498/wJ6WAf7fXx4nNzOdt6+vtDo0y3S6vHzihwc52TkCwMdvqOdLb1+Nwz4//s0vJ91hZ3NtEa829fPs6R4lnUREJCV1uLy8fLaPEx3DTJ04V5iTzpISJ1VF2Tgz0sjOcJCd7iAYjjDmDzLmDzHiDdA1PE7XsJczgwugAAAgAElEQVShsQDhCFwY8HBhwMPTp3oozE7n9lUVbKgpxG5j3lwLTnCN+XnudC8A1zWUUDUPBlgT7YalpYDJxYEx2gbHqCmeP+0XZGFR0kkkCZztGcXjD2EDNs2DqXVTZaU7ePC+rXzoe/s42ubiT39+hJwMB7euLLc6tDm3r2WAT//kMAMePw67jb9+5xo+sqPO6rDmxMaaQl5t6mdfyyAeXxDnPK3qEhGR+ccfDPPzA208eaJrMtlUkJ3O9UtLWFtVQFHOzBb/8PpDnO/3cK53lHO9bgY9flzeAI8cbueRw+2U52Wyrb6YTbWF5GSk/t/LcCTCI4fa8YfC5Gam8eZYk2y5urVVBeRnpTEyHuS15n7uKdZsAUlNqf8pJjIPHLoYnVq3rDyXguz5t6R8bmYaD318G/d8Zy9mzyh/9ONDPPjRrdy8oszq0OZEJBLhh3su8re/PUUwHKEgO51v3buJXcsXxusHWLM4uoqdPxRmd1O/VqsREZG42NM8gD949Slu16LT5eV7r7bQ3OcBoDwvk5tXlLG+unDWVcrZGQ5WL85n9eLoQhu9o+Mcax/mTNcIncPj9I76ePx4F7872c26qgJ2NJSkdJXL7qZ+Wvqj5+89m6rIzlhYvaxmy2G3sXNpCb872cPupgG1qJCUpaSTiMV8gRBne0aBaDVIqsnP+u+PEafTecXtCnMy+NED2/ngd/fS3Ofhkz88yL/ft40bl5fORZiW8fpD/OWjJ3jkUDsAKxfl8d2PbKW2JHUvHmcjJyONrfVF7G0Z5AWzT0knERGJC38wnLAFOA5dHOTRxk6C4Qg2G9y8oozbVlbEfUp8eV4Wt6/K4vO3LedQ6xBPnuimsc2FLxjmSJuLI20u6opz2LW8lJWV+SnVC+nigIenT0Z7WG6vL2ZVbEVbmZ4blpXyu5M9vNbcTyQSmXfTLmVhUNJJxGJmzyjBcASH3ZaSf4jTHfbJUUaXawyAZk/fFbf/7JuW89Unz9A9Ms79PzjAF968YkavOyPNzs6lJdcc93Rdywhq+9AY//Ji8+TF8Lb6Yj5x4xLO93s4Hxvxm6oif36v6narUc7elkFeNHt14SQiIkltT8sAjx3tBKAiL5MHdjUkfGq4zWZjSWku79pYxVvXLuJY2zB7zw/QNTzOxcExLu5rpcSZwU0rythcW5T0/SDHAyH+7aVmQpEIpbkZvG3dwu3pOVvXL40Ozva7/Zg9o6xclHrfFUSUdBKx2ERD6WVluWSlp2a58cQoY09PtBl6BVdfgeW+6+t58JUWBj1+vvaMyYd31LG8PG9ax6oqnNvGk7MZQY1EIhxuHeI3RzsJhCLYbfCWNYu4cVkp/R7/Ffcrcc6sJ0SqedPKcr765Bm6hsd14SQiIklrasKpoczJV9+9jvEEVlRdTmaag21LitlaX0RLv4dXz/Vj9owy4PHzqyMdvGj2csuKcqqyIkmbfPrKE6fpHB7HboMPbK0hI81udUgpZ2mZk0X5WXSPjLO7aUDXTpKS9JsvYqFAKIzZHZ1at2bxwvkjUpCdzgM3LqHYmUEgFO13dKpz2Oqw4sIXCPGLQ+381+EOAqEIhTnpfPKmpexaXrbgK3uWledOJg2fP9NrcTQiIiKvt6e5fzLhtLTMyUd31Fu6+IXNZmNpWS73XV/P525bzqbY6nZDYwF+1djBQ0cGOd7tJRhOXF+r2fjR3ov8cM9FAG5bVUF10cJqKxAvNpuN65dFK/xfa+q3OBqR2VHSScRCTb1u/KEwdhspObXuWhTmZPDJXQ2U52USCkd4eH8rjW1DVod1TbqGvXz7xSYa26IVX6sr8/nMrcupTeHmn/Fks9m4dWW0efqLZ648BVNERMQKR1qHeOxYFxCtQP/Ijvqkqs6pyM/i/Vtr+PztK9hcW4TdBqO+MM+3uPnHp8+y7/xAUiSfnj3Vw189egKI9itdKAvHJMoNsSl2e1sGCISs//cVmakZpe0Nw7ABTwCPm6b5rStssxjouMxDZaZp9se2+Qvgj4E84FHgT0zTdM0kFpH54ERHtLqnvtS5IJeQz89O5xO7Gvj+a+fpdI3zi4PteANhdjbMXc+meIhEIuy/MMjjx7om+3PduXYROxtKFnx106VuNcr58d5WDrUOMTwWoCBn/q3WKCIiqadnZJxfN0a/wjSUOfnwjrqkSjhNVZKbyfu2VHOrUcaTR1s53TuOyxvg0cZOXjT7uNUoZ3NdIWn2uY//aJuLz/z0COEIrK8u4NM3L71qawF5YzcsiyadPP4Qx9pdbKkrtjgikZmZ9ieRYRh24JvAW99g09WAC6i85Gcg9jyfBj4LfBy4Nbb992YauEiqC4UjnO6O9nNau7jA4mis48xM44EbG6grziECPHa0kyeOdxGORKwObVpGxwP8cM/FydVtip0ZfOqmpVy/tFQJp8vYubSEjDQ7oXCEV5pU7SQiItbzBUM8vL91clr8h7Ynb8JpqpLcTN68LI+Pbi5mS1208mnYG+DXjR380zNnOXhhkFB47q6nLg54+MOHDuANhKguyubf79tGZor2K00miwqyWFoWXSF6d9OAxdGIzNy0Pk0Nw2gAXgLuIppQupo1wBnTNLsv+Zn4xPsi8Hemaf7ONM2DwH3A3YZh1M7yNYikpJY+N+OBaIns6gU2te5SWekOPn7DElYtijYTf7Wpn4f3tc561bi5crxjmK8/dw6zJ9qXa311AX9y6zKqiua22XkqyclIY0esku0FTbETERGLRSIRHm3spG/Uh8Nm44PbasnOSK1ESWGWg7s3V/OFNxtsiU27GxoL8MsjHfzTs2c53DqU8OTTgQuDvOdfXqPf7acgO50ffHw7ZXnze1XeuTRR7bRbfZ0kBU03hb8DOA5sBt6o2+8a4MzlHjAMYxHQALw8cZ9pmseBIeCGacYiMi9MrFpXW5xDframGGWk2fnQjjpuWBpNSJzqGuHBV1oY9gYsjuz13L4gPzvQyk/3tzLmD5Gd7uCebTX8wbbalF2BcC69yYj2dnjpbC/hORyBFRERudTBi0OTvRjfunYRNSnch7HYmcHdW6r509tXsKmmEBsw6PHzyKF2vv7cWY4kKPn0yKF2PvTgPgY9fgpz0vmPj21lWXlu3I+zkE20nmhsc+ELhiyORmRmptVExjTNh4GHAQzDeKPN1wAewzD2AbXAQeDzpmk2AVWxbS7t+dQFVE8zZpGUF45EONkVTTotpFXr3ojdZuPt6xdT7Mzgt8e66HB5+ebz53j/lhqMWBWUlSKRCIdbh3jieDfeQPQP/oqKXN67qVqJwxm4dWU5f/3YKfrdfk50DrO+utDqkEREZAEacPsmV6pbszif65emVk/JKynNzeT9W2u42Sjj+TO9HG8fpt/t5xeH2nn6VA9vXbOITXWF5Gdd27XLeCDEPz1zlu+83AJEV/v79/u2UV/qjMfLkCm21kf7OPmCYU50DKuvk6SURHQuXgWYRKfRBYH/DbxkGMYaYGLowHfJPj5gVvWXoVAI0zRnGWrqGhsbA1iQrz3e5uJcOp1OXK4xenqiI2kdI348viAAFRl+enq643Ysd6EDt9tPT8/cTF2aejy/P9oo8lpfT0MuvHNVAb87N8KYP8RDey6wtSqbnbVOnBTR3u7D4/HEIfqrm/rvNjgW5IXzbtqHo5VXmWk2dtU5WV2ehXdkAO/ItR8vEedyusdLNCeFv/fvtig3jW53kN/sPUPmuqKEHVeflfFj1bkMhTSiKyKJ8fjx6AIgBdnpvHdT9bzrxViel8UfbKvlVmOc58/0cqJjmGFvgJ8fbOPx4128a+Ni3r2pKjolzz791x4IhfnFwXa+8dw5ukfGAbhpRRnfunfTNSey5PLK8jJZUurkfL+HAxeGlHSSlJKIpFM1EDJN0wdgGMY9QCvwPqAxtk0mMDpln0xgLAGxiCSllsFoQqHMmUZBlqZjXU59UQb3bijiybMjdI0GOdjhpWMkwEdzcthSNXel7x5fiBdb3Bzr9jJRkL6iJJObluTizEj+JqPJan1lNt3nRjnW7eX9CUw6iYiIXI7ZPcKZ7ujXkTvXLkq5Pk4zUZGfxQe31zLo8bO7uZ/DF4dw+4L8ZF8rP9nXSlVhNnetr2RbfTHrawooz8t63XMEQ2FOdo6w//wgP9l3kQsD0a9uGQ47f7hrCV988wrSHLouSqRt9UXRpNP5QT5181KrwxGZtrgnnUzTHLvkttcwjPNEk1G/jd1dCUztglbJ66fcTYvD4ZjOlL95Z2KkeSG+9nibq3PZ7Omjgugf8bbjZwFYV1NMRUVFXI+T68zFh5+Kirm5eJp6vImqnIqKRXF57grg09WLefpUN6+c66drNMg/PN/GcDCdz962jJyMROTNo8YDIX6yr5V/fPYiHn+00qLYmcFd6ytZuSgxUyITeS7f6HiJVliYTXV12eTtt3qcPH3uKKf6/CxdtjxhF6r6rIwfq85lY2Ojqp1EJK6CoTC/PdYFQEOpk3VVC2MV4WJnBu9Yv5iPXFdH57CXRxs7Od4xTIfLy3debpmcJldZkEVNcQ4Omw27HQLBCCc7hyevhwDsNrh7czWfu3051UWp2wcrlWytL+Y/D7Zz8OIQ4XBkRtVpIlaK6zc2wzCqgFPAXaZpvhK7Lw9YAXzNNM1uwzBagF1EG5NjGMY6oBDYE89YRJLVoMdP32h0hqlRYX2fomTnsNu4c20lKyry+PWRDgY8fv7tpWYeO9rJn71lBXetX0x6HBMWI+MBfrz3Iv/x6nn63dGKtMw0O29aWc7OhhKN4sXJdbGGmG5fkJOdI2yoUV8nERGZG7ub+hnw+LHb4B0bFs+7aXVvxJmZxgO7GnhgVwNNvW5+09jBC2YfZ7pHCIQidA2P0zU8ftl9q4uyuX5pCZ+8qYFl5bqOnUvbY32dhr0BzvW6k6Lfqch0XHPSyTCMAiDDNM0+0zQ7DMM4DHzDMIxPAV7gK0A38MvYLt8AvmwYxsXY/Q8CvzBNs/VaYxFJBWd7oqXcORkOqoqyLY4mdSwty+Wzty3nSKyRd4fLy+d/fpR/+N1ZPrFrCfdcwxLHkUiEk50j/PJwB7841MboeLTfVobDzq7lpVzXUEJuZuKqqhaiqsJsaotzaB0cY2/LgJJOIiIyJ4a9AZ43e4HoimAV+a+fSraQLCvP5Qt3GHzhDgNfMMTprlGOtbsYcPsJRyKEI9HmAisq8thWX8ziQl27WqWuJIfS3Ez63T72XxhU0klSRjy+RX0duAWoj93+APAPwGOAE3gWeItpmhPrnn8DKAV+AKQTnXL36TjEIZISzFj/gBUVedgX2MjatUp32HnPpmo+e9tyvvbMWZ48Hl3h7q8fO8XXnjnLLUY5t68q5+YVZRTmZFz1ubz+EMc7htnXMsCjRztp6nVPPpaT4eBD19XywK4GznSN0uHyJvqlLUg7GoppHRxjT8sAf6TeBCIiMgd+d7KbQChCbmYat62Kb4uDVJeZ5mBjTSEbNRCUlGw2G9uXFPHE8W4OXhjkIzvqrA5JZFpmnHQyTbP+ktsfu+R2H3DfVfaPAH8Z+xFZUAKhMC390eSGptbNXkNZLt++dzMX+j1895UWHjnUzuh4kMeOdvLY0U7sNqgvdVJVmE11UTZleVn4AiFGfUHc40Fa+t2c7holFI783vMuLXPy3s3V3Lu9liJnNGl1pmv0ciFIHOxcWsJ/HmznwPlBgqGwpi6KiEhC9bt9HG2LriR8x+oKstLnb/NwmZ+21RfzxPFuDpwftDoUkWnTfBGROXS+30MgFMEGLC/PtTqclFdf6uQr71nH529fwTOnenjudA+vNvXjC4Zp6fPQ0ud5w+dYUurkFqOM926qZm1V/oLr62Cl65ZE+zp5/CFOdI5oZFVERBLqpbN9RICinHQ21WrlVEk922J9nTqHx+lweanSdEdJAUo6icyhial1NcU55KhHUNyU5WVy73W13HtdLV5/iP0XBjnf56bD5aV9yMuA2092hoPcrDTyMtMoz89iU20hG6sLJyuaZO4tLsymriSHiwNj7GkeUNJJREQSxjXm50jrEAA3rSjDoZW/JAWtXJRHbmYabl+QA+cHqdpUZXVIIm9I33pF5kgkEsGMNRFX47/Eyc5wcPOKMm5eUWZ1KDINO5aUcHEg2kz8f9yivk4iIpIYL5/rIxyBvKw0NqvKSVJUmsPOptpCXjnXz4ELg7xbSSdJAWqgITJHekbGGfT4gWgTcRGJ9nUCOHhhkEAobHE0IiIyH42OBzh4IVrltGt5GenqISgpbHtsit2BC+rrJKlBn7gic+Ro+zAAeZlpVBYs7OV5RSZc1xC9cPL4Q5zoGLY4GhERmY9ebeonGI6Qk+GY/MIukqq2LYm+h8/2uBmKDWiLJDMlnUTmyNH26GopKyrysKtZtQgAlQXZ1JfkALCnZcDiaEREZL4Z8wXZ1xKtCLlxWSkZafr6I6ltY00h6Y7od4mDF4csjkbkjelTV2QOjPmDk03EV6ifk8jv2dEQnWK3p1lJJxERia8DFwbxh8Jkptkn/96IpLKsdAdrqwoAONyqpJMkPyWdRObAvpZBguEINmBZWa7V4YgklYkpdkdaXYTCEYujERGR+SIcibA/1vdma10RWekOiyMSiY+JZvhHlHSSFKCkk8gceLWpH4DqomyyM3TBIzLV1rpo0sntC3I2tsKjiIjItWrqdTM0FgD+uw+OyHywqbYQgGPtwwS1EIskOSWdRObA7ljSaVm5ptaJXKq6KJvS3EwADqk3gYiIxMn+89EqpyWlTsrztIiLzB+bYpVOY/4QZ3vcFkcjcnVKOokkWO/oOGdi/ZyWlWtqncilbDYbW+qiI3bqTSAiIvEw4g1wpnsEQCvWybyzuCCL8rzogN2RNl07SXJT0kkkwV5rijZHzkizU1OcbXE0IslpS110xO6wKp1ERCQODl4cIhyBnAwHaxbnWx2OSFzZbLbJKXZHWl0WRyNydUo6iSTYRD+nlRV5pNn1KydyORNJpwsDY/S7fRZHIyIiqSwciXAw1kB8S20RaQ5df8n8s0nNxCVF6BNYJIEikchkP6c1saVNReT11iwuIN1hAzRiJyIi1+ZszygurxqIy/y2qSZa6dTc52E41jBfJBkp6SSSQM19HrqGxwFYU6nSbpEryUp3sDaWmFUzcRERuRYTDcSXljknF6oQmW/WVRfgsEcH7BrbNWAnyUtJJ5EEmqhyKs3NpLpI/ZxErmZLrfo6iYjItRkdD2DGFnDZvqTE4mhEEicnI42Vi6IrY2uKnSQzJZ1EEmiin9ONy0qw2WwWRyOS3DbH+jodbXcRCIUtjkZERFLR8Y5hIkBWup1VsS/kIvOVmolLKkizOgCR+SoYCrO3Obpy3Q3LSi2ORiT5TTQT9wXDnOocYUOsV4GIvDHDMGzAE8Djpml+6wrbLAY6LvNQmWma/YmMT2SuHGsfBqK9AtVAXOa7TTVF/HhvK41tLsLhCHa7Brkl+eiTWCRBjrYPM+oLAko6iUxHRX4WVYXRaajq6yQyfYZh2IFvAm99g01XAy6g8pKfgYQGKDJHhjx+WgfHANhQrYGLK8nPUt3BfDFR6TTsDXB+wGNxNCKXp08ckQSZ6OfUUOZkcWE253rcFkckkvw21xXR4fJyuHWI+1lidTgiSc8wjAbgIaCGaELpatYAZ0zT7E54YCIWONYRrXLKzUyjocxpcTTJK91hZ0/zAP5gYqeyV+SriXuiLSl1UpCdzrA3wJFWF0vLcq0OSeR1lHQSSZD/7uekKieR6dpSW8hjRzvVTFxk+nYAx4F3AYffYNs1wJmERyRikWOxFbzWVRVgVy/Nq/IHw3S4vAk9RokzI6HPL2Cz2dhUW8iLZh9HWod435Zqq0MSeR0lnUQSYDwQojHW0O/6pUo6iUzXlrpiADqHx+ka9lJZoFUfRa7GNM2HgYcBDMN4o83XAB7DMPYBtcBB4POmaTbN5tihUAjTNGeza1IYG4tOw0rl15DsEn2OnU4nLtcYPT0uBseCdA2PA1DjDNHTE7+CPnehA7fbT09PX9yeM17H8fv9ADN6vcn8epLxOBPn2O1xz8nrcVJIe7sPj2d60+VqckIA7D3XjWmmXqJPn8WJN9tzHAqF4nJ89XQSSYBDF4fwh8LYbHDdkmKrwxFJGSsr88hKj/5pOnxRK7GIxNkqIA/4IvAeIAK8ZBiGmt9IyjP7fQDkZdpZlKtxdVk4VpVlAXB+yM94QKv/SvLRJ7JIAuxtifZkXbkonyKVFotMW7rDzvrqQvafH+Rw6xBvX19pdUgi80k1EDJN0wdgGMY9QCvwPuB7M30yh8MxneqqpDUx4pvKryHZzcU5bvb0UR7JpPnoWQA21ZawaNGiuB4j15mLDz8VFY64Pm88jjNR4VRRMf3XnMyvJxmPM3GO5+r1FBZmU11dNu3tF9UE+N9PdxKOgM9ZwYYUG/DWZ3HizfYcNzY2xqXaSZVOIgmwpzmadNrZUGJxJCKpZ2NNtOhiojeHiMSHaZpjEwmn2G0vcJ5oMkokZXW4vAx4olOgNtQUWByNyNwqyEmnviQH0LWTJCclnUTibMwf5GjsA3/nUiWdRGZqYpnrEx0jBEMqExeJB8MwqgzDGDYMY9eU+/KAFcBp6yITuXbH2qOr1pXlZbIoP8viaETm3vrYtdPR2O+CSDJR0kkkzg5eGCIQimCzwfb61CpvFUkG66ujo9TeQIimPrfF0YikLsMwCgzDKAMwTbOD6Op23zAM4zrDMNYDPwW6gV9aGKbINYlEIpzqGgFgfVUBNq1aJwvQxLWTKp0kGSnpJBJnE/2c1izOpyAn3eJoRFJPdVH25DLLR9t08SRyDb4OHJhy+wPAMeAxYA8QAt5immbAgthE4qLD5WUwNrVuVWW+xdGIWGNDrDXBxYExXGN+i6MR+X1qJC4SZ3ta1M9J5FrYbDbWVxfwgtnH0fZh7tlmdUQiqcE0zfpLbn/sktt9wH1zGJJIwh1pjQ5OFOakU1mgqXWyMK1ZnI/dBuFIdLrpTSum34hcJNFU6SQSR25fcLKvgPo5iczexIidKp1ERORqjrQNAbBqUb6m1sm8kJ8187qQnIw0VlTkAZpiJ8lHlU4icXTgwiChcAS7Dbapn5PIpJleQE00Eze7RxkPhMhKT+zyxCIiknp6R8Zp7vMAmlon80e6w86e5gH8wZktplKel8mZ7lGeP9PLuqrCae2TkWbXQLkknJJOInE00c9pXVUBeVnq5yQyYaYXUG5fEIBgOMJP9l5kWXnejI6niygRkfnvuTO9AGSl21lS6rQ4GpH48QfDdLi8M9qnKNYPs6nXPe19qwqzZxybyEwp6SQSR3ubo0mnHfqyK/I6M72AKspJZ2gswJE2F9kZM/tzpYsoEZH575lTPQCsqMjDYdfUOlnYqgtzABgZDzLiDZCfrQFwSQ4zuoo3DMMGPAE8bprmt66wTRHw98BdQAbwAvC52FK9GIZxPbD7kt08pmnmzjB2kaQyMh7geEesn5OaiItcs+qiHIbGhmkfmtlIn4iIzH8eX5BXm/oBTa0TAagoyMRhtxEKR2gf8rJaSSdJEtNuJG4Yhh34JvDWN9j0+8BG4F3ALUAe8BvDMCYacqwGjgOVU34aZhS1SBI6eGGQcATS7Db1cxKJg+qiaLVS+9CYxZGIiEiyeeVcP/5gGIfNhlExsynYIvNRmt0+uYJju0vXTpI8plXpZBhGA/AQUANcsR2+YRilRJNNO0zT3B+7736gHVgLHAXWACdN0+y+ttBFksu+lkEA1lUX4MzUzFWRa1UVSzr1u/14/SGyM9RMXEREoiam1q2szNNiEyIx1UXZtA956VCVuCSR6VY67SBanbQZGL7Kdl7gbUDjlPsmusZONNhYA5yZQYwiKWHv+WjS6bolmlonEg9VhdlMdOiYaTNNERGZv4KhMM+fiSadNtUUWRyNSPKY6OvUPuQlEolYHI1I1LTKMUzTfBh4GMAwjKtt5wGevOTuLwAjwLHY7TWA2zCMo0AJ8BLwxdlWPoVCIUzTnM2uKW1sLFoyuRBfe7zF41x6A2GOt0eLAKvSx173XE6nE5drjJ6eKxYKxo270IHb7aenpy/hx7r0eH6/H4CensQVMjoppL3dh8fjSdgxJo81h/9uMPfnci7fK7M9VnG2gwFviNNtPeRF3NPeb+J9os/K+LHqXIZCoTk9nogkvyNtLobGAgBsri1kfIZLy4vMVxNV4t5AiEGPn5LcTIsjEknw6nWGYfwB0aTTn5imOWYYRgGwGEgHHgCcwFeApwzD2GaaZiCR8YgkyqneccIRsNtgTUWW1eHMa0W5WWRlzc05zsrKwp7mm5NjyeVV5KUz4A3R6w5aHYqIiCSJF81eAFYuyqMkN1PVsCIxZXmZZKTZ8QfDtLu8SjpJUkhY0skwjI8B3wP+2TTNfwUwTXM4lnjymKYZim33HqATuBl4dqbHcTgcV62+mq8mRpoX4muPt3icy8cuRJ9j9eJ8Nq9bfdltmj19VJD4ZEmuMxcffioq5qa/wdTjTVTlVFQsStjxigsKODccxB9M/B/RCkcG+bn5VFTMzeofc30u5/K9MttjLfOkc6q3k76x8IzORWFhNtXVZZMVcfqsvHZW/d1pbGxUtZOI/J6Xz0ZXrbt5Rdn/z96dh8l11/eef5+qrt6ql+p919Ky9bO1WPIq2cay2TEhjkl4SGBygayPB0ju3MDNkBBmcnMZbiaB3LBkkgyEhJDEuXAhGILBHhssb7K8qbVZOtqlXtSLWr1v1VV15o9T1W7aLam6u6pOLZ/X8/QjupY+3y7a3ae+5/v7/DyuRCS7+CyLtlAZZy9N0Tsyw472kNcliaSn6WSM+U/AXwB/atv2Hyy+z7bt8SWfDxhjhoH2dNQikgkvxvOc7tigPKdMCEdiGbmqWRcsTvsx5Oo6atxsgvHZCOMz81Rp+18RkYJ2aXKOw71uxOyezXF5R7wAACAASURBVA1EosqtEVmsPd500u6/ki2SDRJPWny3ur8A/mhpw8kYs9sYM2GMWbfotnVAPXAs1bWIZMLsfJSubjfzZ1dnrcfViOSXpqoS/JYbJ96n5RMiIgXv2ZPulFNZwM9tGxQiLrJUItepb2yWmMLEJQusedIpvlyu2LbtIWNMM/Bl4GHg7+KfJ4wAB3CX0n3dGPN7QCnwJeAp27b3r7UWES90dY8SjroBlrdvUNNJJJWK/D6aqkvoG52ld2yGG1qqvC5JREQ89PQJd0OKOzfVUVKUmSgBkVzSFnKbTuFIjEuTczRWKm9WvJWKSacvAi/F//d7gHLgA8DFJR9vt217DrgfmMTdte5x4ATwvhTUIeKJ/WfcpXWbmyqo1XIskZRrrY5fsRvRpJOISCGLxRyePuk2nfZcX+9xNSLZqTZYTGnAfZuvKXHJBiuedLJte8OSzz+y6H9/DTc8/GrPPwM8uNLjimSrF88NA7Bro/KcRNKhNVQG50foG5v1uhQREfHQaxfHuTQZBuBe0+hxNSLZybIsWkNlnBlyw8R3dmgZqngr5ZlOIoUkHInxyvkRAO7YqKV1IumQGBMfm5lnci7icTUiIuKVvfGldR21ZWyoK/e4GpHslTh3ysTGOyLXoqaTyBoc7h1jdt7Nc9qlppNIWjRXl+Jzs8Q1Ji4iUsASeU57rm/Aim8yISJvlGg6KUxcsoGaTiJr8OJZN89pY32QxiqF9ImkQ8DvWwjBVNNJRKQwTczOL0yX37u5weNqRLLbz4SJT8x5XI0UOjWdRNZg/1k3z+kO7VonklatIbfppDFxEZHCtO/0MJGYQ5HP4s5NytEUuZrFYeI6dxKvqekkskrRmMPL59wrbrs61XQSSafWxJi4TpxERApSIs/plvU1VJYGPK5GJLslwsRB507iPTWdRFbptb7xhVBjhYiLpFdiTHxkep6ZcNTjakREJNOeOXkJ0NI6kWS1x8+detR0Eo+p6SSySomldW2hMtprtIOKSDo1V5eSiIzVmLiISGHpvjzNhcvTANxzfb3H1YjkhsSk08VRhYmLt9R0ElmlRIi4dq0TSb+SIj/1FSWAxsRFRArN86fdKaeq0iK2tlZ7XI1IblgIE48qTFy8paaTyCrEYg4vnnObTlpaJ5IZbTWJ7X/VdBIRKSTPnXKny3d31uH3Wdd4tIiAwsQle6jpJLIKJwcnGZ2eB2BXp3ZQEcmE1mp3BztNOomIFA7HcXj+tNt0uvs6La0TSZZlWQvTTmo6iZfUdBJZhUSeU0NlCRvqlOckkgmJbIJLk2Fm5xUmLiJSCE4OTnJp0l0adNcmXegTWQk1nSQbqOkksgr7F+U5WZbGvEUyIdF0Arg4NuthJSIikinPn3LznBorS7iuscLjakRyS1t8s6O+0RmFiYtn1HQSWSHHcRQiLuKB0oCfumAxoCV2IiKF4rn40rq7NtXpQp/ICiUmneajDkMKExePqOkkskJnL00t/NK+Y6PGvEUyKTHtpKaTiEj+i8YcXjiTaDopz0lkpWrKA5QF/IDOncQ7ajqJrFBiyqmmPMD1GvMWyahWZROIiBSMI71jTMxGALjrOl3oE1mpxWHiPTp3Eo+o6SSyQok8p9s31OLTtr0iGZU4cRqamCMciXlcjYiIpFNi17p1teW012jjFpHVWJgSH1HTSbyhppPICi3kOXXqiptIprVWlwLgAP1jOnkSEclnz592Q8Tv1pSTyKq11cSbTmMKExdvqOkksgI9I9MLy3oUIi6SeeUlRYTKAwD0agc7EZG8NReJ8tI590Kf8pxEVk9h4uI1NZ1EVmD/Gffkp7KkiBtbqjyuRqQwtVZrTFxEJN91XRhldt5dRn3nJk06iazW4jBxZWKKF9R0ElmBxNK62zbU4Feek4gnFo+Ji4hIfkrkOd3QXEl9RYnH1YjkrsVh4mo6iRfUdBJZgf1n3ROgOzbqipuIVxKTTgPjs8xHFSYuIpKPXjjjnnPtVoamyJotXLDTlLh4QE0nkSQNjM9ybngagF2dynMS8UpryA0Tjznuf5ciIpJfZuejHOgeBWC3zrlE1mxhBzuFiYsH1HQSSVJiaV1ZwM/2tmqPqxEpXJWlAapKiwCNiYuI5KOD3aOEI+4k6+0b1HQSWSuFiYuX1HQSSVJiad2t62sI+PWfjoiXFq7YjWrSSUQk3+yPX+i7vrGCOuU5iayZwsTFS3rnLJKkxKTTHRt1xU3Ea683nXTiJCKSbxIX+hRnIJIalmUt5Dr1KtdJMkxNJ5EkXJ4Kc2JgElDTSSQbJMbE+8dnicaUTSAiki/CkRivnB8BYJc2bhFJGe1gJ15R00kkCYkpp2K/j50dIY+rEZHEpFM05ihMXEQkjxzuHWV23s1z0qSTSOokmk4Xx2Z0wU4ySk0nkSQkmk47O0KUxtdDi4h3qkqLCJa4YeJaYicikj9eOOOec3XWB2msLPW4GpH88TNh4pMKE5fMUdNJJAnKFhDJLpZl0RZy34z0janpJCKSLxIh4jrnEkmt0KIw8T7lOkkGqekkcg3js/O8dnEcUJ6TSDZprdYOdiIi+SQSjfHKuXjTSXlOIillWRbtNcp1ksxT00nkGl4+dxnHAb/P4pZ1NV6XIyJxrcomEBHJK0f7xpkKRwFNOomkQ6vCxMUDajqJXENizHt7W/VChoyIeG9xNsElZROIiOS8RJzButpyWuLTrCKSOgoTFy+s6B20McYCHgV+aNv2V67yuE8DHwMqgUeAj9u2Pbro/t8EPg00AT8BHrJtu2fl5YukXyJEfJeW1olklUQ2wcx8lN7RGZqqFDgrIpLL9p/ROZdIOrXV/GyY+Lraco8rkkKQ9KSTMcYHfBl41zUe91Hgd4FfA94MbAG+tuj+dwN/CXwK2A0EgH+LN7REssp0OMLhnjFAY94i2cayLFrjYeIXNSYuIpLTojGHFxN5Tp3KcxJJh1BZgPJiN0y8V2HikiFJNZ2MMZ3AXuA9wOg1Hv4J4LO2bT9m2/bLwIeBXzLGrFt0/9/atv0/bNs+BHwQ2AG8aTXfgEg6vXJ+hEjMwbLg1vVqOolkm9ezCRQmLiKSy45dHGdiNgJo0kkkXdzdf5XrJJmV7KTTbuAwcAswdqUHGWOagU7g6cRttm0fBkaAu+PTUruX3D8MHEFNJ8lCiTHvra1VVJcFPK5GRJZaHCYec5RNICKSqxIZmq3VpQs7bIlI6iWaTn1qOkmGJJXpZNv2vwD/AmCMudpD2+L/9i65/SLQDtQA5Ve5XySr7DvjBlru1ra9IlkpceI0F4lxeSpMfUWJxxWJiMhq7I+fc+3qrMOylLohki7a/VcyLdVbcSWSyJZuIzQHlCRx/4pFo1Fs217NU3Pa9PQ0QEF+76l2pddydj5G14URADpKZtf0WgeDQUZHpxkYuNbq1LWbDPmZnAwzMDCU9mMtPV44HAZgYKA/I8dLN72W2X8sx3Eo9luEow7Hzl9kc72b8RQkRE/PnH5XppBXr2U0Gs3o8UQk82KL85y0tE4krRaHiWvaSTIh6SDxJCV+apc2kEqA6STuF8kaRwdniTpgAduatCuWSDayLIuGoHv9ZHAy4nE1IiKyGicGJxidngcUIi6SbovDxM8NT3lcjRSCVE869cT/bQEuLbq9BXdJ3TBuc6llyfNaWJTztBJ+v/9aS/7yUuJKcyF+76l2pdfy++eOA7C1rYpbb9qy5uOcnhqiifQ3ryqCFcwRpqnJn/ZjLT1eYiqnqak5I8dLN72WuXGsDQMxeseHGZ33LbxeoVAZ7e0NTE25J1P6Xbl2Xv3d6erq0rSTSJ5LZGg2VpawoU5buIukUyJM/OTgJGcvqekk6ZfSSSfbtvuBM8A9iduMMduBELDPtm0H2L/k/jpgG/B8KmsRWasX4idAynMSyW6tC4GYszgKExcRyTn7zyrPSSSTEpmYmnSSTFjzpJMxphootm07EdTxJeBPjDHngX7gq8C3bdu+EL//i8C/GmMOA13A54EDtm0/s9ZaRFJlOhzhYLebv7RbY94iWS3RdJqZjzI6PU9NsNjjikREJFmO4/DiWeU5iWRS4tzpwuVpItEYRf5Up+6IvC4VP11fBF5a9PmXgL8G/gF4EngN+K3EnbZtPwL8Z+C/AfuAMPDeFNQhkjIvnxshEnPwWXC7ToBEslp9RQkBv3tlvFeBmCIiOeX00CSXJt3NM3Z36pxLJBPaF4WJnxyc9LgayXcrnnSybXvDks8/suRzB/hM/ONKX+MrwFdWemyRTHkhvm3v1tZqqssCHlcjIlfj91k0V5XSPTJD39gM29qqvS5JRESStD8+5VRfUcymhgqPqxEpDNXxMPHpcJTDvWPc2FLldUmSxzRHJ7KMRNNJV9xEcsPruU6adBIRySWJEPE7NtYqz0kkQxJh4gBHesc8rkbynZpOIktMzUU41OP+8lWek0huSJw49SpMXEQkZziO83qIuDZuEcmotvgSu8T7HpF0UdNJZIlXzr+e53TbBk06ieSCxKTT1FyEidmIx9WIiEgyzg9PMzA+B7iTTiKSOYkLdscujhOJxjyuRvKZmk4iSyjPSST3NFaV4I8vy9ASOxGR7BAMBgkGg1e8PzHlFCoPYJoqM1WWiPB602kuElOYuKTVioPERfKd8pxEck+Rz0dTdQl9o7P0jqnpJCLitX2nhxm8NA3A6amhZR/zSFcfAJ31QZ45eWnFx2iqKll9gSIFrrosQGVpEROzEQ73KExc0kdNJ5FFFuc53blJ2QIiuaS1uoy+0Vn6Rme9LkVEpOCFIzHODIwC0ETpso95rW8cgObqMnpXMaVaFyxefYEiBc6yLDbUBTncO8bh3jHef3uH1yVJntLyOpFFXlaek0jO0g52IiK5Y2QqzOjMPAAb66+8BE9E0mdDnfvf3mHtYCdppEknKXiLswYSS+u2tVVTVao8J5FcksgmGJuZZ3x23uNqRDLLGGMBjwI/tG37K1d53KeBjwGVwCPAx23bHs1MlSKvO3tpCoDSgI+W6uUnoUQkvRIN39cujjMfjRHwayZFUk9NJylY+04PE47EGB19PW/g8aP9gPvmda+9fP7AaihzQCT9mqtL8VkQc+D88JTX5YhkjDHGB3wJeBfww6s87qPA7wIfAoaB/xf4GvC+DJQp8jMSTaf1tUF88Y0gRCSzNtSVA+5y2JMDk2xpVa6TpJ6aTlKwwpEYvaMzDMTzBkKRwMIJUENlyaqyBa5EmQMi6Rfw+2isLKV/fJZzw9NelyOSEcaYTuAbQAdwrYmlTwCftW37sfhzPwwcMsass237QnorFflZZy65u2V1NmhpnYhXaoPF1AWLGZ4Kc6R3TE0nSQvNz4nEnR+eJuaAxevrm0Ukt7SG3CUamnSSArIbOAzcAlwxlMMY0wx0Ak8nbrNt+zAwAtyd5hpFfsbodJiRaeU5iXjNsiy2t1cDcKhXK60lPTTpJBKXmHJqDZVRGvB7XI2IrEZrqIxXL4xq0kkKhm3b/wL8C4Ax5moPbYv/27vk9otA+2qOHY1GsW17NU/NCtPT7u+JXP4eslUwGGR0dJpwOAzAwED/z9x/fMjdZbTYb+GfG2NgYHxVx5kM+ZmcDDMwkLpIhFw7zpVe41QfZzXy5TiJ13hyajIvvp/FgoRYX+kub33p1AC2nfnVGfpdnH6rfY2j0WhKjq9JJ5G4M0Ma8xbJda3Vbpj40MQcY9MKExdZpDz+79yS2+cABQ9KRvWMub+fWyqLlOck4jHT6P55ODMSJhJzPK5G8pEmnUSAcDS2kOHUqTFvkZzVUl2KBTjA0Ytj1HldkEj2SAQVlgATi24vAVY1Guj3+681XZXVEld8c/l7yGanp4YojudmNjU1/8x9/QfdlaA3tNbS1NS46mNUBCuYI0xTU3on1LP5OIkJp6WvcaqPsxr5cpzEa5wv389ioVAZdzRXwKPnmI86UN2Caa1O+3EX0+/i9Fvta9zV1ZWSaSdNOokAfeORhTyn9cpzEslZJQE/dRXu0MbR3tUt1xDJUz3xf1uW3N7CG5fciaTN+Ow8w1PuciXlOYl4r7mqlPoKd1ndkd4rRgOKrJqaTiJAz7h78tNWozwnkVyXCBM/0qcTJ5EE27b7gTPAPYnbjDHbgRCwz6u6pPAkMjQDfou2mvJrPFpE0s2yLLa1udNNh9V0kjRQ00kE6B3TDioi+aIt5OY66WqdFDpjTLUxpmHRTV8C/sQY83PGmFuBbwDftm37gjcVSiFKNJ3W1wbx+5TnJJINbko0nXp07iSpp0wnKXjhaIyByQigPCeRfNAabzqduTTFzHyMsoCur0jB+iJwH7Ah/vmXgHrgH4AA8O/ARz2oSwrY2SG36bRB51wiWSMx6XSsf4L5aIyAX+dOkjpqOknB6xuP4AA+S3lOIvkgsYOd48CZy3NsbSrzuCKRzLBte8OSzz+y5HMH+Ez8QyTjJmbnGZp0N1DUdLlI9tje7jadwpEYJwYm2JrhMHHJb2phSsHrGXPznFpDynMSyQdlxX4a4mHip4aX7g4vIiJeOTfsbpRY5LPoqNEFAZFs4YaJu+dOWmInqaamkxS87nieU2d9hceViEiqrK9zw2lPqukkIpI1zl6aBGBdbTlFWr4jkjUsy2J7WxWgMHFJPf22l4I2HY4wOOXmOV3XqKaTSL7YEF8qq0knEZHskQgR19I6keyzPZ7rpI1YJNXUdJKCdiYeZum3Xp+MEJHcl/jv+fxomLlIzONqRERkai7CwLjynESy1fb2EADHLk4Q1rmTpJCaTlLQTg+5Y94tlQHt0iCSRxKTTjEHzo6EPa5GRETODccv9PksOmp1oU8k2yQmncJRN0xcJFX0LlsK2qlBt+nUEQp4XImIpFJVWYCW6lJAS+xERLJBYmldR02ZLvSJZKGmqhIaK90w8YM9ox5XI/lEv/GlYA1PzjE85U5ArKsu9rgaEUm1xHa/ChMXEfGe8pxEsptlWezscJfYdV1Q00lSR00nKVivXRwHoNhv0VhR5HE1IpJqiTHxU5fUdBIR8dJMOEr/2CwAG7VbsEjW2hFvOmnSSVJJTScpWEf73KZTe3UAn2V5XI2IpNpN7W7T6ezIHLPzUY+rEREpXOeGp3AAnwXrlOckkrVujjedTg5OMjE773E1ki/UdJKC5DjOwqRTh5bWieSl7fGmU9SBY/H/3kVEJPMSS+vaa8opLtLbD5Fsta29GssCx4HDvWNelyN5Qr/1pSCdGpxkbMbt3ndUK0RcJB/VV5TQGHSXzh7q0YmTiIhXlOckkhuqSgNsanCXwHZ1a4mdpIaaTlKQnj11CYDK0iJqy/weVyMi6bK5XruwiIh4aS4So290BlDTSSQXJMLED6rpJCmippMUpOdODQOwqaECS3lOInlrc30pAIc16SQi4om+ifmFPKf1ynMSyXqJMHFNOkmqqOkkBScSjbH/zOtNJxHJX4lJp1NDk0zORTyuRkSk8PSOuXEGraEySgKaLhfJdokw8YHxuYVdJ0XWIql94o0xRcAXgA/Gn/NN4JO2bYeXPO4+4KdX+DIftm37H40xdwHPLblvyrZtvfuXjDjUO8ZE/M3npoYgcxPaTl0kX11f5zadHAeO9o6xq7PO44pERApLz7jbdNLSOpHcYJorKSnyMReJ0dU9wruqW7wuSXJcspNOnwPuBx4AHoz/+7llHvc80LLk46vAGeB78cdsAQ4veUzn6soXWblnTrh5Ti3VpYTKtXOdSD6rKPHTVuVuFqAwcRGRzApHYwxOuhf61HQSyQ0Bv49tbe4OwF3dOneStbvmpJMxphT4KPAB27b3xW/7HeBhY8xnbNueSTw2PvnUv+i5twK/Dtxn23Ziv+qtwFHbthceJ5JJz5wcAmBba7XHlYhIJmyuL6F3fJ5D2vpXRCSjesfdPCcL2FCnppNIrtjRHuKV8yN0dY94XYrkgWQmnXYCQeDpRbftjd+28xrP/TPgf9q2/eyi27YCx1dSpEiqjM/OcyAeipfo4ItIfkuEiR/SDnYiIhnVPeourWuvKaNUeU4iOWNHh/s+6XDPGNGY43E1kuuSyXRqw81cWrhEbNv2uDFmGmi/0pOMMbuBN+Mup1tsKzBpjDkI1OE2sD6x2smnaDSKbdureWpOm56eBijI730tnj8/STTmUOSzaCmL0jvQTzjsRpMNDKRv+G4y5GdyMszAwFDajuHFsZYeT69l6o6n13JtgoTo6ZljenqadRXuydL54WlePvQalSV647MaXv3diUajGT2eiKRO95j7t0wbt4jklps7agCYCkc5PTTJ5qZKjyuSXJbMpFM5sFzS8hxQcpXnfRT4kW3bC1NNxphqoBUIAL8J/CqwEfixMSaQbNEiq/VKn/umaXtLOSVF2rxRpBB01gTwWe7/PnFJGweIiGTCxOw8l6bdpvGmRjWdRHJJR20ZtUE3+7brgibFZW2SmXSaYfnmUgkwvdwTjDHFwHuB31h8u23bY/HG05Rt29H4Y98L9AH3Ak8kX7rL7/djjFnp03Je4kpzIX7va3HokT4A3ratnVComiZKFyZJmpqa03bcimAFc4Rpakr/hEUmj7X0eHotU3c8vZZrEwqV0d7ewNTUFOXA9Y2V2AMTjFiVGHNdRmrIN1793enq6tK0k0gOOnZxAgC/Betqyz2uRkRWwrIsdrRX81N7iK6eUd5/e4fXJUkOS2bUowcIGmMWZuqMMVW4E1C9V3jOHtyG1qNL77BtezzRcIp/PgAMc5WleiKpcH54iguX3T7pnusbPK5GRDLppnY3m0C5TiIimfHaRTeZo6UqQMCv6XKRXLOjIwTAAU06yRol8xfgIDAF3LPotnvjtx28wnPuBF61bXty8Y3GmN3GmAljzLpFt60D6oFjKylcZKWePnkJgJryAFtbqzyuRkQyKdF0OtyjHexERDLhtYvuxtUd1UrQEMlFN69zc53s/nGm5iIeVyO57JpNJ9u2Z4CvAl8xxtxjjNkDfBn4K9u2Z40x1caYpWMjNwFHlvlyB3CX0n3dGHOTMeYO4FvAU7Zt71/TdyJyDc+ccAOM33R9A75EwIuIFISb2t2rdX1jswxNKNdJRCSd+kZnGBh3f9euqy72uBoRWY2dHSEsC2IOHOzWtJOsXrKzrp8CHgd+AHwXeAT4dPy+LwIvLXl8E3B56RexbXsOuB+YxN217nHgBPC+lRYushLz0Rj7Tg8DcM/19R5XIyKZdkNLJQG/22zWiZOISHo9d8qdLi/2WzRWJBMhKyLZproswOZGN2HnlfMjHlcjuSypvwLxZtFD8Y+l931kmdv2XOVrnQEeTL5EkbU72D3KRHwsVHlOIoWnpMjPlpYqDvaM0dU9ytu2NHldkohI3kpc6GurCuCzNF0ukqtuWR/CHpjg1QtqOsnqKdVPCkIiz2lzUwXN1aUeVyMiXkhkE3Rp0klEJG0cx+G50+55l/KcRHLbLfFzp1cvjBKLOR5XI7lKTScpCE/H85zu0ZSTSMHaGd+F5WC3TpxERNLl9NDUQp5Th/KcRHLarevdptPYzDxnLk1e49Eiy1PTSfLe5akwB+PbpO/ZrKaTSKG6eZ3bdJqYi3B6SCdOIiLp8Hx8yqmqtIi6cr/H1YjIWmysD1JT7k4svnpek+KyOmo6Sd575uQQjgOlAR+7NtZ6XY6IeGRdbTm1Qfeq+4ELOnESEUmHRIj4jS1VWMpzEslplmUtLLFTmLislppOkveest2ldXdtqqc0oCtuIoXKsqyFJXYHlOskIpJy89EYz59yQ8S3tVZ7XI2IpMIt8SV2ryhMXFZJTSfJa7GYw954ntN9RkvrRArdQtNJJ04iIim3eLfgbW1qOonkg8Sk06nBScam5z2uRnKRmk6S1w71jnF5KgzAfZsbPa5GRLyWyHU6MTDBVPyNkYiIpEZi45bNTRULy5lFJLft6KjG73OXyr7arYt2snJqOklee8oeBKCzIci6unKPqxERr93U7jadYg4c6hnzuBoRkfyy96Sb57RHuwWL5I3y4iK2tFQBcEC5TrIKajpJXvtpPM9JU04iAlBdFuC6xgoAupTrJCKSMiNTYQ5pt2CRvHRLfFJcuU6yGmo6Sd4anpxbOPlRnpOIJCjXSUQk9Z49dQnHgZIiH3dot2CRvJIIE++6MEo05nhcjeQaNZ0kbz1z0j35KQv4dfIjIgsSuU4HukdxHJ04iYikQiLPaVdnnXYLFskzt8abTlPhKHb/hMfVSK5R00nyViLP6a5NOvkRkdclJp2GJuboG5v1uBoRkdznOA5Pn3SbTnuur/e4GhFJtbZQGY2VJQC8cv6yx9VIrlHTSfJSNOawN37FTUvrRGQx01RJWbwRrSV2IiJrd2JgkoHxOQDuVZ6TSN6xLIvbNrjTTi+d07mTrIyaTpKXDvWMMjI9D8B9RiHiIvK6Ir+P7e3VgJtNICIia5NYWtdSXbqwWYOI5Jc7NrhxJS+evax4AlkRNZ0kL/30uLu0blNDkI7aco+rEZFsszjXSURE1ub1pXUNWJblcTUikg53bKwDoH98lu7LMx5XI7lETSfJS08cc5tOb72xyeNKRCQb3dzhjogf7h1jLhL1uBoRkdw1E46y/6yb8bJHS+tE8pZprqSqtAiA/WeHPa5GcomaTpJ3+kZneO3iOABvU9NJRJaR2IUlHIlxpHfM42pERHLX/rPDhCMxfBbcfV2d1+WISJr4fRa3L1piJ5IsNZ0k7zx5bACAUHmAW+JLaEREFmuoLGFjfRBQIKaIyFr8JB5psLMjRKi82ONqRCSd7tgYbzqdU9NJkqemk+SdxNK6N5tGivz6EReR5d0Wn3Z6WU0nEZFVcRyHJxVpIFIwEk2n88PT9I/NelyN5Aq9I5e8MjUXYd9pd43xW2/UrnUicmWJrX9fOa9dWEREVsMemKB31A0UVqSBSP7b1lZNWcAPaNpJkqemk+SVZ05eIhyNUeSzFGYpIld1WzyXYGR6R+7EcwAAIABJREFUntNDUx5XIyKSexJTTm2hMjY3VXhcjYikW8DvW8jFfEm5TpIkNZ0krzwRz3Pa1VlLVWnA42pEJJt11gepDbr5Iy/rap2IyIolcjTfdmMjlmV5XI2IZMJCrpOaTpIkNZ0kb0RjDj+Nh1lqxFtErsWyrNev1inXSURkRS5NznGgexSAt+i8S6RgJJpO9sAEI1Nhj6uRXKCmk+SNru5RhuO/+NR0EpFk3L4o10lERJL3lD2E40B5sZ/dnbVelyMiGbKzI0RxfLOmlzQpLklQ00nyRmLEe3NTBR215R5XIyK5IJHrdG54msEJ7cIiIpKsxHnXPdfXU1Lk97gaEcmU0oCfHR3VgJbYSXLUdJK8kchz0pa9IpKsba3VlBS5fwpf0RI7EZGkzEWiPH1iCNB5l0ghWsh10qSTJEFNJ8kL54enODEwCbhhliIiySgu8rGjIwTAy+fVdBIRScaLZy8zFY5iWfBmo/MukUJzx8Y6AI70jjExO+9xNZLt1HSSvPDY0X4AGipLuLmjxuNqRCSXJHKdtIOdiEhynjzmbtyyoz1EQ2WJx9WISKbdur6GIp9FzNESO7k2NZ0kL/z4iNt0eseWJnw+bdkrIsm7bb07In60b5zpcMTjakREspvjOK9HGtygKSeRQlRRUsTO+KT4c6eGPa5Gsp2aTpLzBsdnefWCu2XvO7c2e1yNiOSaW9bVYFkQiTl0xbf/FhGR5R3tG6dnZAaAt21RnpNIobrrunoAnjt1yeNKJNup6SQ577HX3KttVaVF7O6s87gaEck11eUBTFMlAC+dVa6TiMjV/OjIRQA21ge5obnS42pExCtvijed7IEJ7QAsV6Wmk+S8x+JL6952YxPFRfqRFpGVu32Du8TuhTMaERcRuRLHcfjRYfe86/5tzViWIg1ECtXOjhBlAT8A+07r/EmurCiZBxljioAvAB+MP+ebwCdt2w4v89hWoHeZL9Ng2/al+GM+DXwMqAQeAT5u27bWNMiKjU6HF94kvkNL60Rkle7cVMc3XzjPKxdGmJ2PUho/iRIRkdedGJjkzKUpAO7f1uJxNSLipeIiH7s6a3nKHuK5U5f4hZ1tXpckWSrZsZDPAfcDDwAPxv/93BUeuwUYBVqWfAwDGGM+Cvwu8GvAm+OP/9rqypdC9+SxQSIxh9KAj3s3N3hdjojkqMTS3HAkxoELugYiIrKcRw+7S+vaa8rY1lblcTUi4rW7NyVynYZxHMfjaiRbXXPSyRhTCnwU+IBt2/vit/0O8LAx5jO2bc8secpW4Lht2/1X+JKfAD5r2/Zj8a/1YeCQMWadbdsXVvuNSGH68VH3x+y+zY2UFWsyQURWpzZYzA3NlRzvn2DfmWHu3KR8OBGRpRK7Bb97e4uW1okId13nni/1js5wfniaDfVBjyuSbJTMpNNOIAg8vei2vfHbdi7z+K3A8eW+kDGmGehc/LVs2z4MjAB3J1eyiGs6HOHpE0MAvHObdk8RkbVJTDu9oFwCEZE3OD00iT0wAcC7tinSQETgxuYqaoPFADyrXezkCpJpOrUBU7ZtjyVusG17HJgG2pd5/FagzRiz3xhz0RjzA2PMdYu+Frwx8+niFb6WyBXttYeYi8Qo8lm85QY1nURkbRLTTQe6R5gJRz2uRkQkuySmnFqqS9nZHvK4GhFJharSpCKer8jnsxbOn54/raaTLC+Zn7JyYG6Z2+eAkmVuvxGwcZfRRYA/BPYaY7bGv1biucl8rWuKRqPYtr2ap+a06elpgIL83hO+tc89+dnRUkb/hTNcaT3ncoLBIKOj0wwMjBIOu3n4AwMr+QorMxnyMzkZZmBgKG3H8OJYS4+n1zJ1x9NruTZBQvT0zK3od2VdNIoFzEcdvvfcIW5pLb/mcwqJV393olE1AEWyQSLP6V3bmvH5tLROJB8E/D72nR4mHImt+ms0Vrhv4/eeGOKnxwfxLbP0trjIp+iCApZM02mG5RtCJbjTTku1A1HbtucAjDG/DFwA3gd0LXruRBJfSwpEaWkpfn/ymUxzkRgvdLs/Mm/dXEswuLL1w6WlpfiKluulikihqizxs6m2hFOX5zh4cUZNJxGRuAvD0xztGwe0a51IvglHYvSOLo1pTl5dvOk0NRfl5fMjtIXK3vCY5W6TwpFM06kHCBpjKm3bngAwxlThTi0tXSaHbdvTSz6fMcacxW1G/Xv85hZg8fxdy3JfKxl+vx9jzGqemtMSV5rz6XtfSZf9pXOXmZmP4bcs1jc3cHoqsKJjNfmLqaqooqkpsDBJ0tSUvnyCimAFc4Rpakp/2Hkmj7X0eHotU3c8vZZrEwqV0d7ewNSUu7V3sr8r79sS5dSzZzkx6uTV79dU8OrvTldXl6adRDz26BF3yqmhsoRb19d4XI2IZJPaYDE15QFGpuc5PTipBpO8QTJNp4PAFHAP8Gj8tnvjtx1c/EBjTBvwGvAe27afid9WCWwGvmDbdr8x5kz8ax2O378dCAH71vzdSE5bSZf9qXiA+KbGIGOzEcZmIys6Vl088E5EZLE7N9XxtWfPcqhnjKm5CMGStWUdiIjkg0e6+gB419Zm/FpaJyJLbGqo4OXzI5wanGTP5gavy5Esc80gcdu2Z4CvAl8xxtxjjNkDfBn4K9u2Z40x1caYhvhje4FXgS8ZY3YZY24CHgb6ge/Gv+SXgD8xxvycMeZW4BvAt23bvpDy707y0lwkit3vjnhvb1OQpYikzu0ba/FZEIk5vHTustfliIh47nj/OMcuuuddD97cdo1Hi0gh2txUCcDZ4SnmIppOlp+V7CXcTwFlwA9ww8H/Gfh0/L4vAvcBG+Kfvx/4fPyxQeAJ4J22bc/H7/8SUA/8AxDAXXL30dV/C1JojvdPMB918PsstrRUeV2OiOSRqtIA29uqOdgzxr4zw9xnGr0uSeSqjDFFwBeAD+Ke130T+KRt2+FlHtvK8nEGDbZta9shWdb3DrhTTuvryrllnS72icgbXddYgc+CaMzh9OAkW1qrvS5JskhSTad4KPhD8Y+l931kyedDwIev8rUc4DPxD5EVO9wzBsD1jRWUFWcmH0ZECsfuTXUc7BnjhdPDXpcikozPAfcDDwDFuBPkYeCTyzx2CzCKu9PwYvphl2XFYg6PdLl9ygd3tmEtsyuViEhpwM+GuiBnLk1hD0yo6SQ/Q2EVklNm56OcGHA3PrypXb/MRCT17uys42/3nuFw7xjjs/NUla5sowKRTDHGlOJOi3/Atu198dt+B3jYGPOZeETCYluB47Zt92e4VMlR+89e5uLYLKCldSJydaa50m069U/gOI6a1LLgmplOItnk2MVxIjGHIp/Fjc1aWiciqXf7hloCfouYA8+f0gCIZLWduFEGTy+6bW/8tp3LPH4rcDwDdUme+LcDPQDs7AixsT7ocTUiks1MPNdpfDay0KwWAU06SY45FF9aZ5orKQloaZ2IpF6wpIjb1tey78wwT58c4l3bmr0uSeRK2oAp27bHEjfYtj1ujJkG2pd5/FZgyhizH1gHvAz8J9u2T63m4NFoFNu2V/PUrDA9PQ2Q099DOs1FYvz7QTfP6a7WohW9TsFgkNHRacJhN1psYCA9w3WTIT+Tk2EGBobS8vVz4TireY2z+fvJxuMkXuPJqcm8+H7SdSzHcagq8TE+F+OV0334219vVAcJ0dMzx9TU1Buep9/F6bfa1zgaTU0ovCadJGfMhKOcGpwEYHubltaJSPrc627Kyl57CMdxPK5G5IrKgbllbp8DSpa5/UagEvgE8F7AAfYaY5QOLW+wv3ua6fkYPgvu7az0uhwRyXKWZbGhphiAcyNv2MtCCpgmnSRnHOkbI+o4BPwWN2hpnYik0b2bG/jTHx2nd3SG00NTXNdY4XVJIsuZYfnmUgkwvczt7UA0vkEMxphfBi4A7wO+ttKD+/1+jDErfVrWSFzxzeXvIZ3+/IWXAbjPNLJrx5YVP//01BDFA6MANDWlZ2K0IljBHGGamtI7/Z7Nx0lMOK3kNc7m7ycbj5N4jfPl+0nnsW52ghzqP0f/RITKUD3lJW67IRQqo729Ydnn6Hdx+q32Ne7q6krJtJMmnSRnHLjgnrhsba2muEg/uiKSPjc0V9JY6b6X33si/ePtIqvUAwSNMQtjKMaYKtwJqN6lD7ZtezrRcIp/PgOcZfmleFLALk+FecoeBOC9ChAXkSR1NgQJ+C0c4ER8hYqI3rlLTrg8FebcsLsGeGeHVgGISHpZlsW9m+NL7NR0kux1EJgC7ll0273x2w4ufqAxps0YM2aMuWfRbZXAZuBYBmqVHPLdV3uIxBwqSop4241NXpcjIjki4PfRWe9Oh9v94x5XI9lCTSfJCV3dIwBUlhaxqUHLXEQk/RK5TvvPDDM7n5ogRZFUik8qfRX4ijHmHmPMHuDLwF/Ztj1rjKk2xv1Btm27F3gV+JIxZpcx5ibgYaAf+K5H34JkIcdxePjFCwA8eHMrZcXauEVEkmea3eHbEwOTxJSLKSjTSXKA4zgLS+t2tIfw+yyPKxKRQvCm6+rxWe4OTi+cGeY+0+h1SSLL+RRQBvwAiAD/DHw6ft8XgfuADfHP3w98Pv7YIPAE8E7btuczV654bd/pYcKR2BXvt/snOD3kTpdvbqxkr73yac+mquWixkSkEJjmSjgIM/NRui9Ps74ueO0nSV5T00myXvfIDMNT7g4IN6/T0joRyYxQeTE7O0K8emGUvSeG1HSSrBTPaHoo/rH0vo8s+XwI+HBmKpNsFY7E6B2dueL9jx65CEB7TRlFft9VH3sldcHiVdcnIrmtpryY5qpS+sdnea1vXE0n0fI6yX4HLrhL65qrSmmpLvO4GhEpJPdudhtNynUSkUIwHY5wpHcMgDs21HpcjYjkqi2t7k7jR/rGcLTEruCp6SRZLRKNcajHPfnRlJOIZNqezfUAnBmaovvycjvQi4jkjwMXRonEHEqKfGxvr/a6HBHJUdva3N8fI9Pz9I3NelyNeE1NJ8lq9sAEM/NRLGCHdq0TkQy7qT1EqDwAaNpJRPKb4zi8dO4y4J5zlRQpQFxEVqepsoT6CjfbLTE9KYVLTSfJaokA8esaK6gqDXhcjYgUGr/P4p7r3V3snlpFmK6ISK64cHmawYk5QEvrRGRtLMtiW2KJXa+W2BU6NZ0ka03ORbD7JwAtrRMR77zZ3XGeZ08NMROOelyNiEh6vHjWnXJqC5XRGlKGpoisTWKJ3fBUmJ6RlW9IIPlDTSfJWq+eHyHqOJQGfGxtVa6AiHjjLTc04vdZzM7HeOakpp1EJP9MzM5zSAHiIpJCLdWl1MZ3snzp/GWPqxEvqekkWclxHF6O/3La2VFDwK8fVRHxRqi8mF0b3Tdhj7824HE1IiKpt//sZaIxh7KAXxmaIpISlmWxNb7E7qVzIx5XI17SO3nJSmeHp7g0GQZ0xU1EvPfOrc0APHlsgEg05nE1IiKpMx+Nsf/MMAC7OmspLtLbAxFJjW3x1Sp9ozOcGpzwuBrxiv6qSFZ6KZ4r0FFTRnN1qcfViEihe/uWJsDd+ldX60Qkn3R1jzIVjuK3LHZ31nldjojkkfaaMqrL3M2gfnS43+NqxCtqOknWmQ5HONo3DsDtmnISkSzQGirjpnb3at1jR3XSJCL5wXEcnjt1CYCb2qu1U7CIpNTiXewePaLzp0KlppNknQMXRonEHEqKfNzUrlwBEckO74hPO/1/rw1o618RyQsnBycZnJgD4O7r6j2uRkTyUWIXu2MXxzkxoCV2hUhNJ8kqjuPw0jl3ad2OjpByBUQkayRynXpHZxamMUVEclliyqmzIUhrqMzjakQkH62rLaehsgSA77za43E14gW9o5escuHy9MIVNy2tE5Fscl1jBRvrgwA8riV2IpLjBsZnOTk4CcCbNmnKSUTSw7Is7o7/jvnegV6iMU2LFxo1nSSr7I8HiLeGSmnTFTcRWaWq0iIAgsEgwWAwJV/TsizesdVdYvfY0YGUfE0REa88fWIIgPqKYjY3V3pcjYjkszfFl+8OjM/xbHzCUgpHkdcFiCSMToc53DMGwO6N2j1FRFYv4Pex7/Qwg5emATg9NZSSr9tY4Y6H2wMTfOulbpqq3N01i4t83LlJv7dEJDdcmpyjq3sUgHs3N+CzLI8rEpF81lBZwh0ba3nx7GW+80oP925u8LokySA1nSRr/MQeJOo4lBf72dGhAHERWZtwJMaZAfdNVROlKfmaJQE/laVFTMxGePL44MJJkyYzRSSXPGUP4QA15QF2dtR4XY6IFIBfuqWNF89e5rGj/YzPzmu3zAKi5XWSFeYiUX56fBCAOzbUEvDrR1NEso/PstjW6u7CcjA+JSAikksGJ2bp6h4B4D7TiN+nKScRSb93b2+hNOBjLhLj0UMXvS5HMkjv7CUr/ODgRcZnI/gs2NWpJSoikr12xicx+8dnuTg243E1IiIr84ODF4k5ECoLcPM6TZaLSGZUlgYWdgLWLnaFRU0n8ZzjOHz92bMAbGurprpMo5Yikr3aa8qoCxYDmnYSkdzSfXma5+IhvveaBop8eisgIpnzS7e0A/DSuRHOD095XI1kiv7SiOdePHuZ1y6OAyxspykikq0sy1qYdjrYM0bM0da/IpIb/nrvaaKOQ3VZgFvXKctJRDLr7uvqaY5vwvKdVzTtVCjUdBLP/f1z5wDY1BCko7bc22JERJKQaDqNzcxz7pKu1IlI9uu+PM23X+4GYM/mBoqUnykiGeb3WfziLW0APPxSN+FIzOOKJBP010Y81X15msdf6wfg7VuaPa5GRCQ5dRUldNS4O9Z1aYmdiOSAP3vMZj7qUBss5rb1mnISEW98cNc6fBYMTczx46P9XpcjGaCmk3jqb/aeJuZAS3Upt2/QCZCI5I4d8WmnI31julInIlntYPcoPzjYB7iZKtolWES80l5Tztu3NAHwjefPeVuMZERRMg8yxhQBXwA+GH/ON4FP2rYdXuaxNcCfAe8BioGfAv/Rtu3e+P13Ac8tedqUbdsVq/0mJDcNjM/y7Zfdtby/vadTYZYiklNuag/x6OGLzM7HONgzytu3NnldkojIGziOw+cePQbAjS1V3LWpjotjsx5XJSKF7MN3beCxowO8cn6Ek5eCXF9f6nVJkkbJvsv/HHA/8ADwYPzfz13hsX8P7AR+AbgPqAS+b4zxx+/fAhwGWhZ9dK6idslxX336DOFojLpgMb9y+zqvyxERWZGKkiKua3Svl+w7PexxNSIiy3vy2CD7z14G4A/ffQM+y/K4IhEpdHd21mGaKgF45NiYx9VIul2z6WSMKQU+CnzCtu19tm3vBX4HeMgYU7bksfW4zaaP27b9om3bh4FfB24BtsUfthU4att2/6KPwRR+T5IDLk+F+ef9FwD4jXs2Ulbsv8YzRESyz+u72I1yeeoNw78iIp6KRGP86Y+PA254+D3XN3hckYiIuxPwh+5aD8BTZycZnY16XJGkUzKTTjuBIPD0otv2xm/bueSxM8C7ga5FtyWCLhINqq3A8RVXKnnl7587y8x8lKrSIv7D7vVelyMisipbWqopDfiIxJyFXaFERLLFt17u4dTgJJYFf3D/DV6XIyKy4MGdbVSWFjEfdfjxCU075bNkMp3acDOXFn4SbNseN8ZMA+2LH2jb9hTwoyXP/z1gHDgU/3wrMGmMOQjU4TawPmHb9qqi66PRKLZtr+apOW16ehogJ7/3qXCUrz97HoCfv6GSvvNnCAaDjI5OMzCQmV2gJkN+JifDDAwMEQ670wkDA+nbPWHx8dItk8daejy9lqk7nl7L1BwvE6/jDfUldF2c4RvPnWFP03zeLl3x6u9ONKqrnyKrMTod5vOPu/+9/uLN7dzYUuVxRSIirwuWFPH+2zr4u2fP8u/Hx/l0NEaRNjnIS8n8v1oOzC1z+xxQcrUnGmN+Bbfp9CnbtqeNMdVAKxAAfhP4VWAj8GNjTGAlhUvu+v6xMabCMUqLLB68MeR1OSIia7K9yQ2/7BsP80rvtMfViIi4/uwxm8tTYSpKivj9dxmvyxEReYMP3bkeCxiaivDDwxe9LkfSJJlJpxmWby6VAFc8uzbGfAT4GvCXtm3/NYBt22PxxtOUbdvR+OPeC/QB9wJPrKh6wO/3Y0zh/SFNXGnOte99fHae7/0Pd8rpQ3dt5PYdNy7cd3pqiCYys3NBRbCCOcI0NfkXJiCampozcrx0y+Sxlh5Pr2XqjqfXMjXHKy4uBtL7OjYBN/RHON4/wVM9MX71rbn1ezlZXv3d6erq0rSTyAoduDDCwy+62Zm/9/bNNFVpZygRyT7r64LcvT7Is+en+PJPTvHzN7Xi8+XnxHghS2bSqQcIGmMqEzcYY6pwJ6B6l3uCMeY/4e5i9+e2bX9i8X22bY8nGk7xzweAYZYs1ZP89Ld7TzM6PU+w2M9v79GmhSKSH95yQyMAPzk+QO/ojMfViEghi8Yc/uh7R3Ac2NJSxYfuVHamiGSvD+6oBeDU4CQ/OpK+OATxTjJNp4PAFHDPotvujd92cOmDjTG/DvwF8Ee2bf/Bkvt2G2MmjDHrFt22DqgHjq28fMklA+Oz/N2zZwH4rT2d1FdcdXWmiEjOuHVdDfUVJcQc+Nf4dIGIiBf+6YXzHO0bB+Cz792mjBQRyWqb6kq4c10QgC//5CSxmONxRZJq1/wrZNv2DPBV4CvGmHuMMXuALwN/Zdv2rDGm2hjTAGCMaY7f9zDwd8aY5kUfJcAB3KV0XzfG3GSMuQP4FvCUbdv70/MtSrb4yydOMjsfo76imN+8R1NOIpI/ivw+fuX2DgD+9aVu5qOxazxDRCT1Bsdn+fxj7lLYD9zRwS3rajyuSETk2v6XHe7vquP9Ezz+2oDH1UiqJXvp41PA48APgO8CjwCfjt/3ReCl+P9+D+6yuw8AF5d8vN227TngfmASd9e6x4ETwPvW+o1Idjs1OMm34tuJ/+5br6eiJJk4MRGR3PGBXevwWTA0McfjR3XCJCKZ5TgOf/hvR5iYi1BTHuD333mD1yWJiCTl+vrShaiCLz15EsfRtFM+Seqdf7xZ9FD8Y+l9H1n0v7+GGx5+ta91BnhwRVVKzvv8YzbRmMP6unJ+5fZ1136CiEiOaQuV8ZYbGnni2CB/9+wZ3r29GctSGKaIZMZ3X+3liWNuw/uPH9hKTbDY44pERJL3O2+5jp8cH+S1i+M8eWyQt21p8rokSREt8pa0e+X8CD8+6obCffIdhuIi/diJSH76jTe5S4dfvTDK/rOXPa5GRArFxbEZ/vgHRwF419ZmHtjR6nFFIiIrc/O6GvZsbgDgvz9xQtlOeUTv/iWtojGH//P7RwDY3lbNz21v8bgiEZH02d1Zyy3rQgD81U9PeVyNiBQCx3H4/f95iInZCLXBYj773m2ashSRnPS/ve16AI72jfOdV3s8rkZSRU0nSatv7jvHkV53B5U/fmArPp9OgkQkf1mWxcffch0Az5y8xMHuUY8rEpF89/CL3Txz8hIA/9eD27Q7sIjkrFvW1fDz8UnNP3vMZnIu4nFFkgpqOknaDI7P8oXHTwDuDiq3rtcOKiKS/95sGrmxpQrQtJOIpNfpoUk++8PXAPiFna3cr4lyEclxn7r/BkoDPoYm5vh/dB6VF9R0krT57A+PMTHnjnprBxURKRSWZfGxN28C4PHXBjgxMOFxRSKSj2bno3zsn19lOhylpbqU//LAVq9LEhFZs7ZQGb+9xz2P+tqzZ+m+PO1xRbJWajpJWjx78hLfP9gHuN1q7aAiIoXk/m0tdNYHAXSVTkTS4r/84CjH+yfw+yy+/IGbCZXrXEtE8sND93bSXFVKOBLjc48e87ocWSM1nSTlZuej/B+PuOHht2+o4X23tHtckYhIZvl9Fg/d516l+/7BPs5dmvK4IhHJJ9870MvDL3YD8J/fabhtQ63HFYmIpE55cRGfut9dKfOjI/08f/qSxxXJWqjpJCn3pz86zplLUxT5LP7rg9sUHi4iBem9N7fRFioj5sD//ePjXpcjInni9NAkf/hvhwF4s2ngt+/p9LgiEZGrqyotuuJ9wWCQYDD4htt/YWfrwo7Af/Ddw0yHFSqeq678/77IKuw9McQ/PH8OcLe8vKG5ytuCREQ8EvD7+P13Gf7jv3bxoyP97D8zzK7OOq/LEpEcNjYzz2/948sLOU5feP9OXdwTkawX8PvYd3qYcCT2hvtGR93MptNTQ2+475duaedQzxjnh6f53YcP8B92b7jqcYqLfNy5Seda2UZNJ0mZy1NhPvntgwDcur6Gh+7d5HFFIiLeemBHK3//3Dm6ukf57A+P8cjH7tYbRBFZlUg0xsf/5VXODE1R7PfxlQ/eQq0yM0UkR4QjMXpHZ95w+8DAKABNlL7hPsuyeOuNTTx2tJ8njg2yvi7IpoaKKx6jLVSWuoIlZbS8TlLCcRz+8LuHGZqYI1js57+/fydFfv14iUhhsyyLz7xnCwCHe8f47oFejysSkVz12R8e45mTbq7Jf/vF7dy6vsbjikRE0u9N19XTUeM2k77zag+z81GPK5KVUldAUuLbL/fw46P9APzxA1tZV1fucUUiItnh1vU1PLCjFYA/f+y4MglEZMX+ef/5hfiCh+7dxC/dqk1aRKQw+H0W77u1gyKfxej0PD86ctHrkmSFtLxO1uxQzyh/FN+t7v5tzbxPJ0IiIj/jf7//Bh472s/A+Bx/s/cMv/f2zV6XJCJZ5kp5J4d6RvnLJ04CcHNHiF0ba9lrvzH7JBlNVSVrqlFExAsNlSW8Y2szjx6+yEvnRtjUUMFN7SGvy5IkqekkazI4Pstv/+MrhCMxNtSV86e/eBOWpbwSEZHF2kJl/NY9nXzlp6f4m72neWBHC9c1VnpdlohkkeXyTs4PT/H1584SdRyaq0p5YEcrF8dmV32MOmVAiUiOumtTHSf6Jzg1NMmGAdEtAAAgAElEQVR3Xu2hsbKU5uo35kBJ9tHyOlm1uUiUh/7pFfrHZ6koKeKrH7qN6vKA12WJiGSl//W+TXTUlhGOxPi9bx0kEn3jRIOISMLFsRm+se8c81GH2mAxH7l7AyUBv9dliYh4wmdZ/PLtHYTKA8xHHf5p/3lFFuQINZ1kVRzH4TPfO8KrF0axLPjLX97J9U26ai8iciXBkiL+/H07sCw41DPGXz912uuSRCRLDU/O8ffPnWN2PkZlaRG/fvdGqkp1YU9ECluwpIhf3bWeIp/F5akw33q5m5jjeF2WXIOaTrIqX/nJKb71cg8An3yH4W1bmjyuSEQk++3urOPX794IwBefPMnRvjGPKxKRbDM8OcfXnj3L5FyEsoCfX7t7I7VaFiciAkBrqIz33twG/z979x3e1nkebPwGwb0piaKobUnW0fCQLO8RO9vOno2zm9G02Ukz3DajSTrSr81Om36ZzR5OvsaOY2c4ieM4trxkyZI1jvagBiVx7wHg+wOgTMuSRYoAAZL377p4iTw4OOfBKxDn4XPeAWxv7OQ3qcWslLssOmnUvnHPbj5753YAXnjhbN5x3eIsRyRJE8eHnhuwqLaMwXiCD9z8KH2DLv0rKamxvZev3bObtp4BCvPzeOOVC5lV6ZwlkjTc6vk1XLl4OgD37DjO3dvPbnEFjQ+LThqVHz6wn3++fSsAz1g2k8++8kInDpekUSguiPLZV15IXgS2Heng03dsy3ZIknLAvqYuvn7Pbjp6BykuyOMtV53D/Gml2Q5LknLSDefVs3J2JQC/2XyE+3c3ZTkinY5FJ43YLesP8pFbNgHJ1QO+8tqLKMz3LSRJo7V6fg3vevoSAL59315+9OD+LEckKZvW7Wvm3369je7+GKWFUd569SLmWXCSpNOK5kV41cXzOHdmOQC3PXqIe3cdz3JUOhUrBhqRb9+7h/ffvIFEAtYsqOHrb7iYYldQkaSz9t5nLeVZy2cC8LFbHvMOnTRF3brhIK/++gN098eoKMrnr65ZxOzqkmyHJUk5Lz+ax2svW8CCaaUkSE4Dc8v6g9kOSyex6KSnFI8n+PQdW/nEbVtIJGD1/Gq+9ZeXUFaUn+3QJGlCi+ZF+MKNqwnqKhiMJ3j799dxoLk722FJGieJRIIv/m4H7/3xBvoH48yqLOZtT1tEnXM4SdKIDc1/N7u6mHgC3veTDfzfu3eRcFW7nGHRSafVPxjn/Tdv4Kt/2g3As1fU8cO3Xk5ViUv2SlI6lBfl8403XkxNaQEt3QO85TsP0drdn+2wJGVYd/8g7/vJBj7/u+TCLFcsms7HX7CC6eVFWY5Mkiae4oIob7lqEctmVQDwb7/axidv20IsbuEpF9hdRad0oLmbd/9oPRsOtALJScNfc8l8HtzTnJHz1VWaZEmamuZNK+X/vm4Nr/3GA2xv7OTGr93PD956mX98SpPU9sYO3vmDR9hxtBOAV108j396yXms3dVEa89AlqOTpImppDDKB58T8PMNB7l942G+fd9eDrf18JlXXkhFsZ0mssmik57k148d4cM/e5T23kEiJHs4Xbu0lsPtvRk75/SywowdW5Jy3WWLpvOlV6/mPT9az7YjHcnC019dxswKh9lIk0UikeDmhw/wj7/YTO9AnPy8CH93wzLecvU5rgQsSWlQEM3jyzeupq6imG/du4ffbG5k25E/81+vuYjz5lRlO7wpy+F1OqG7f5BP/GIzf/P9dbT3DjKjvJAPPifgumCmyZAkZdjzzq/nK6+9iIJohB1HO7nxq/dzpC1zxX5J4+dYRx/v+uF6bvp/m+gdiDO3poSfvf1K3nrNInMsSUqjvLwIH3/hCv7lpedRmJ/HvqZuXvaV+/je2r3O85QlFp0EwO+3NvLsz/2Jb9+3F0jOLXDHe66xIixJ4+g5K2fxtddfTGF+HruPd/GS/7qXdftash2WpLM01LvpWZ+7m9s3HQbg+pWzuP0917BqXnWWo5Okyeu1ly3g5++4knNmlNEfi/OxWzfzpm8/5KItWWDRaYo71NrD33xvHW/5zsMcbO2hMJrHB5+zlO+/9TJmunqKJI27py+byTffeDFlhVGOtPfyqq+u5X/u3ePdOWmC2d7Yweu++QAf/tlG2noGqCop4D9ecQH//bqLXJRFksbBytlV3Pbuq3nRhbMB+GN4jGd//m6+8sedDMTiWY5u6nBOpynqaEcvX7lrFz98cD/9g8lfuKuWTOefXnwei2rLsxydJE1t15xbyy/efTVv//46tjd28snbtrBuXwv/8pLzqSr1j1Uplx1p6+Vzd4b8bF0DQwsnvfDC2Xz8BSuorXCBAEkaT+VF+XzxxlXccN4sPnHbZhrb+/j3X4f8/JGDfOi5Ac9eUecw5wyz6DTFHGnr5Vv37uG7a/fSO5AsNtVWFPHR5y/nRRfO9hdOknLE4tpybnnnVfzD/27ilg2H+OXGw6zd1cRNNyzjFRfNJS/Pz2splxxt7+Wb9+7hO/c9nmMtmlHGR56/nGcur8tydJI0dUUiEW44v56rz53B5+7cznfu28uOo5287XvruGBuFe9/9lKuW1rr38IZYtFpCkgkEqzd3cT31u7jt1saiaVuu00rK+Rvrl3E6y9fSElhNMtRSpJOVlqYz+dftYqLF07j03dspamrnw//bCM/enA/n3zRSi6Y65wwUrZtb+zga3/aza0bDjIQS+ZYM8oLed+zlvKqS+ZREHU2C0nKBRXFBfzjC1fy8ovm8pnfhvwxPMbGhjbe9D8PccHcKl5/+QJeeOFsigv82zidLDpNYjuPdnDbo4e5beMhdh/rOrF9Rnkhb7rqHN545ULKi3wLSFIui0QivO7yBTx7RR3/cvtWfvHoIdbvb+VF/3kv15w7g7c9bRFXL5nh3TlpHHX0DvCrTUf42SMNPLin+cT26WWFvPHKhbz56nPMsSQpR503p4pvv+lS1u1r4fN3bufPO4+zsaGND/1sI/9yx1b+4uJ5vHT1HJbNqjC/SoMRXQ2DIMgHPgu8JvWc7wEfDMOw/zT7fwR4J1AB3Aq8KwzD1mGPvxX4CFAH/AH4mzAMG8bwOgT0D8ZZv7+Fe3Yc584tjYSNHU94fM2CGt5wxQKuP28WRflWbyVpIqmrLOZLr17Nqy+dzydv28y2Ix3cs+M49+w4zor6Sl596TxuOL+eGeXOGTOVpDtH0+m19Qzwp+3HuHNLI7/dcuTEEDpIDqN76zWLeNlFc7xDLkkTxJoFNXz/rZexbl8L3127lzs2Haa1e4Cv/Wk3X/vTbhbVlvGC8+u5/rx6ltdbgDpbI70F86/ADcCLgELgO0A/8MGTdwyC4B3Ae4A3AE3A14BvAK9IPf484AvAW4CtwH8APw+C4NIwDF2aZxS6+uPcu/M46/e38Mj+Vu7f3UR3f+wJ+8ypLuEFF9TzolWzWTm7KkuRSpLS5YrF07njPdfwx+1H+erdu3lgTzNbDrfzsVs384+/2MwVi6fzvPPruWrxDBZMLzVBmvzSlqPpiXoHYjx6oJWH97Vwz45jPLS35cQUBQBF+Xk8Z+UsXn7RHJ52bq3zrEnSBLVmQQ1rFtTw0eev4CcP7efmhxvY39zN7mNdfOkPO/nSH3Yyo7yQq5bM4KolM7h4QQ0Lp5f5uT9CZyw6BUFQDLwDeHUYhmtT294N/CgIgo+FYdhz0lM+APxzGIa/Se37RmBjEATzwzDcn3r8q2EY/iT1+GuAw8DVwD1pel2TSlv3APubu9nb1MWOxg62N3by2IEmDrYPkGD3k/ZfNquCpy2t5frzZrF6XrV/cEjSJJOXF+EZy+p4xrI6Hj3QynfX7uO3m4/Q0TfIvTubuHdnEwB1lUVcvmg6q+dVE8yqZNmsCmrKCrMcvdIlAznalJRIJGjtHmDH0U62HWln25EOthxqZ/OhthNzNA0pzM/jikXTuf68WTz/gnoqi11NUpImi9qKIt71jHN559OX8NjBdm7fdJjbNx3iQHMPxzv7uXXDIW7dcAiAiuJ8Lphbxflzqjl3ZjmLZ5azuLaMCq8LTzKSnk6rgDLgT8O23Z3atgpYO7QxCIJZwKLh+4ZhuCkIghbgqiAIGoDLgS8Ne7wpCILHmCJFp3g8QVf/IO29g7R1D9DeO0B7zwBtPQO09w7S0tVPY3svRzv6ONrRR0NLNx29g6c9XiQCS2dWsGpeNZctmsbVS2Yws7J4HF+RJCmbLpxXzWfnVdM3eB5/2n6cX248xN3bj9HaPUBje98TEiSAmRVFLJheypzqEubWlFJXVUxNaQFVJQVUlxRSXVpAVWkBFUX53rTIfWnL0YBJVXRKJBL0DcZp7xmgqauf5mFfTV39tHT1s/vQcQ53DnCsay8dfafPtRbXlnHpOdN5xrKZXLVkOqWFztUkSZNZJBLh/LlVnD+3ipuuD9h9vIt7dx7nzzuOs3Z3Ex29g3T0PvFG35AZ5YXMri6hvqqY2dUlTC8rpLo0mV/VlBZSVVJATVkh1SUFlBZGp0SuNZKr5hygKwzDtqENYRi2B0HQDcw9xb4AB0/afji1bw1Q+hSP54zbNx7mjscOk0gkSCQgkYB4IkECHt9GalsCYvEE/YNx+mJx+gfj9A/G6D/xfeorFn/SHbPRqCjO59yZ5Sytq6Aq0sOimkKed8V5VlMlSRTlR3n2ijqevaKOeDxB2NjB/bubeHBPM5sPtbO/uRvgxE2Nh2h5yuPlRZKrvBQX5FGYnwexQQqiESrKjlOUn0dRfh7RvAjRSIS8vAizq4r5wHMDe36Mr3TmaDkhHk/w5T/sZMfRDuKJBLF4glicE98/vu3x7/sG4/QMxOjtj9EzkPwaPt/SaMytKWHZrAqCWRWsnpccbmHvQEmauiKRCItry1lcW84brlhIPJ5g9/EuNh1s5dEDbWw51M7u450c70xOpXi8s5/jnf1sbGg7w5GTHUiK8vMoLohSnB+luCCPotS/hfl55EUiRPMi5KVyrWgEonkRIpFk/hXNS26/ZGENb7hiYYZb4uxFEomnLoIEQfB64AthGE4/aXsz8J4wDL8/bNs1JO+gVYZh2DFs+yPA/5KcZ2A/cEEYhpuGPf6/QHMYhm8dTfDr1q2LAxkpDXYPxDlD06RPJPkiIiTf1Ml/U19EyDvx/fiKRCLEklW2jJ8rLxI5UdAbD8PPN3TGTLbveL4+23Jinuvk89mW6TlfPHW+jH9+RpKJwHi9vrMVT91Aiadupgxvo3SEXpyfR4ZWh0+sWbPGdedPks4cLQzDfx7NuTOVg8UT0HOWBaMzOZFLDeVakMqxkrlWNqfmGK+ca7w+i8frM3i8X08unuds8oVcfj25eJ6ho0YnyevJxrme6jxpzXmzmI8NfYQPdUqJM5RrJU58tGcyrLLCjKVJY87BRtLTqQc41VI4RUD3KfYdeqzjFPsOf/xMxxqJOJAHtJ/Fc59SaYG5bSKRIA/GqdqVOFFsGx+T+XyT+bWN9/km82sb7/Nl57VFx/F8OV5vAkj9YX1yo+R8t+5Kktd7PVk6c7TRykgOlhfJaOKcs8Yv5xqvz+Lx+gwe39fjeTzP5DrPeJ5r/M6TrXxs6PVNwDzrqaQlBxtJ0akBKAuCoGLozlgQBJWcephcQ+rfeuD4sO31qX2bSCY29Sc9r54nzkcwImvWrHFQvSRJmqrSmaONijmYJEkaiZHcSnoU6AKuGbbt2tS2R4fvGIbhEWD38H2DIDgfqAbWhmGYAB446fHpwHnAfWf3EiRJkqaktOVoGY9UkiRNSWec0wkgCILPAy8G3kiyf9h3gZ+EYXhTEARVQGEYhsdS+74X+Fhq3yPA14EdYRi+KvX4i4EfA28DNgCfAarDMLwsza9NkiRpUktnjiZJkpRuI+0a/XdACXAbMAj8APhI6rEvAtcBC1M/fwmYAXwbKAB+Cbxj6EBhGN4aBMGHgE+TvLt2F/DSs38JkiRJU1bacjRJkqR0G1FPJ0mSJEmSJGk0pt7yIJIkSZIkSco4i06SJEmSJElKO4tOkiRJkiRJSjuLTpIkSZIkSUo7i06SJEmSJElKO4tOkiRJkiRJSrv8bAeg0wuCIB/4LPAakv9X3wM+GIZh/2n2/wjwTqACuBV4VxiGreMUbk4bTVsGQVAD/DvwAqAQuAt4bxiGB8cv4tw02vfksOe9DfhqGIaRzEc5MZzF7/f7gfcB04F7gL8Jw3DfOIWb00b5+10I/Etq31Lgj8B7wjA8MG4BTwBBEESAO4DbwzD8z6fYz+uOcpq5VOaZY2WWuVfmmZNlnrna+MnFHM6eTrntX4EbgBcBL0n9+6+n2jEIgncA7wHeBDwdWAF8Y3zCnBBG3JbA/wCrgBcD15H8RfxFEATRzIeZ80bTjgAEQTAP+I/MhzbhjOb3+2+AfwL+FriU5MX6p+MT5oQwmvflR4GXATcCVwNlwC3jEOOEEQRBHvBl4Poz7Od1RxOBuVTmmWNllrlX5pmTZZ652jjI1RwukkgkMnVsjUEQBMXAceDVYRjeltr2QuBHQG0Yhj0n7b8L+EIYhl9O/Xw+sBFYEIbh/nENPseMpi2DIJgBHAMuD8PwgdS2OUADsCoMw0fHO/5cMdr35LDn/QYoBp7m3bakUb4nI8AB4DNhGH4htW0J8Afgiql+d/gsPis3AD8Jw/DTqZ+vBO4FZoRh2DSuweegIAgWAd8B5gFVwMdOd5fM645ynblU5pljZZa5V+aZk2Weudr4yOUczp5OuWsVyarun4Ztuzu1bdXwHYMgmAUsGr5vGIabgBbgqoxHmvtG3JZAD/A8YMOwbfHUvyWZCnCCGE07AhAEwVuBacDnMh7dxDKatlwBzGHYXbQwDHeGYTjf5AYY/fuyGXhpEASzgiAoAt4A7CT5eSm4HNgEXAS0nW4nrzuaIMylMs8cK7PMvTLPnCzzzNXGR87mcBadctccoCsMwxNvmDAM24FuYO4p9gU4+cPu8Cn2nYpG3JZhGHaFYfirMAz7hm3+W6CdZPV3KhvNe3Koa/engTcDsfEKcoIYTVsuAfqARUEQrA2C4HAQBP8vCILZ4xduThvV+xL4ADCD5OdjF/By4IVhGMZPse+UE4bhD8MwfEcYhs1n2NXrjiYCc6nMM8fKLHOvzDMnyzxztXGQyzmcRafcVUryQ+1kfUDRKfYdeuxM+05Fo2nLJwiC4EaSCdHfhWHYnYHYJpLRtuPXgf9MVc71RKNpywqSn9VfJzn2/SUku8z+OjUp41Q32vflIuAoyUlsrwbWAz8NgqD0FPvq9LzuaCIwl8o8c6zMMvfKPHOyzDNXyy3jfr2z6JS7ejj1f3oRyarwyfsOPXamfaei0bTlCUEQ/CXwfZLjXf87M6FNKCNuxyAI3gLUc4aJLqew0bwnB4AC4KYwDG9LzYPxamAlyUlYp7rRvC8rSY51/3gYhreHYXg/8AqSY9//ItOBTjJedzQRmEtlnjlWZpl7ZZ45WeaZq+WWcb/eWXTKXQ1AWRAEFUMbUr+EpTy5K1xD6t/6k7bXn2LfqWg0bTn0+PtJrrDyH2EYfmBcosx9o2nH1wHLgJYgCDpJjX0PgqAzCILXjlO8uWw0bXko9e9jQxvCMDxGckLGBRmOcyIYTVsuIzl/wInJalPdu7eTvKumkfO6o4nAXCrzzLEyy9wr88zJMs9cLbeM+/XOolPuepTkGNZrhm27NrXtCat7hGF4BNg9fN/UDPTVwNqMR5r7RtyWAEEQvJnk5IsfDcPw78clwolhNO34OpJ3fValvt6T2r4K+EVmw5wQRtOW60nekVgztCEIgpkkx7rvzmyYE8Jo2nIoWbxwaENqRZVFwK4MxjjpeN3RBGEulXnmWJll7pV55mSZZ66WQ7JxvYskEolMHFdpEATB54EXA28EIsB3SS4feVMQBFVAYaq6ThAE7wU+ltr3CMmxxjvCMHxVVoLPMSNty9Rs/ruAW0nOMzBcy0mTX045o3lPnvS8FwC3uWzv40b5+/0Z4EbgTSQvxp8HZgGrwzCc8hOFjrItbwEC4G1AE/Bxkqt9rHBOkScKgmAvyWWh/zP1s9cdTTjmUplnjpVZ5l6ZZ06WeeZq4yvXcjgnPMttf0dyCdnbgEHgB8BHUo99keTY4YWpn79Essr+bZJjjX8JvGPcIs19I23LF5Ds6vnq1NdwLyTZrlPZaN6TemqjacubSE7u9z2Sk1j+HrjB5OaE0bTl60nOd/Ejkt237wGuM4kZEa87mojMpTLPHCuzzL0yz5ws88zVsiur1zt7OkmSJEmSJCntnNNJkiRJkiRJaWfRSZIkSZIkSWln0UmSJEmSJElpZ9FJkiRJkiRJaWfRSZIkSZIkSWln0UmSJEmSJElpZ9FJUlYEQXBBEARPz3YckiRJU5X5mKRMs+gkKVtuBVZmOwhJkqQpzHxMUkZZdJKULZFsByBJkjTFmY9JyqhIIpHIdgySppggCP4IXJv68W7gRuBLwA1AJ3A78IEwDNtS+yeAVwN/DwTAw8DrgA8Brwfagb8Pw/B7qf33po73SmAVsAF4ZxiGj2T8xUmSJOWQIAguAf4NuAzIBzYC7wP+lWH5WBiG1wVBMAtzMklpZE8nSdnwMqAB+IfU9/9L8vPoCuCFwGLgxyc9599IJkiXA/OBR0gmNpeknv/VIAjKh+3/T8D3gIuAEPhtEATTMvR6JEmSck4qN/oVyWLPhSTzqA7gqzw5HwNzMklpZtFJ0rgLw7AZiJFMei4ELgBeG4bhY2EYPgy8Frg+CILzhj3tv8IwvCsMww3AL0neffuHMAxD4HNACXDOsP1/HIbhV8Iw3Ar8NTAIvCrTr02SJCmHlAL/DtwUhuGuVB71FWDF8HwsDMPm1ITi5mSS0io/2wFImvJWkkyImoIgOPmxAHgs9f3OYdu7gb1hGA6ND+5N/Vs0bJ97hr4Jw7AvCIKNwPCESZIkaVILw/BoEATfAN4ZBMGFwFJgNafufGBOJintLDpJyrZ8YB/w7FM81jjs+4GTHouf4biDJ/2cR/JuniRJ0pQQBEE9sA7YSnKY3Q+BmcAPTrG7OZmktHN4naRsGbojthWYTbJr984wDHcC/cBnSSZFZ+uioW+CICgm2V380TEcT5IkaaJ5Gcm86llhGH4mDMPfAXMBgiCI8Hg+BuZkkjLAnk6SsqUTWA7cDGwGfhwEwQdJ3g37MjAN2DuG4/91EAQPk5zc8h9I3pW7eSwBS5IkTTBNQB3w/CAIHgOuAz6aeqyIVD4WBMFM4E7MySSlmT2dJGXLF0kurfsb4EVAM3AXcDfJBOl5YRiOpev1N4G/JZngzAGeGYZhx5giliRJmlhuBr4OfBvYSHIi778i2cNpDcPysTAM45iTSUqzSCKROPNekjSBBEGwF/hMGIb/meVQJEmSpixzMkn2dJIkSZIkSVLaWXSSJEmSJElS2jm8TpIkSZIkSWlnTydJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlnUUnSZIkSZIkpZ1FJ0mSJEmSJKWdRSdJkiRJkiSlXX62AxiLdevWDZIsnLVnOxZJkpQRlUB8zZo1EzpnmWzMwSRJmvTSkoNN9AQuD4hEo9GqbAeSK2KxGADRaDTLkUwMttfo2F6jY3uNju01OlOlvVKv057ZuWfS5WBT5XcqXWyvkbOtRsf2Gh3ba+Rsq9FJVw420YtO7dFotGrVqlXZjiNnhGEIQBAEWY5kYrC9Rsf2Gh3ba3Rsr9GZKu21YcMGYrGYvWlyz6TLwabK71S62F4jZ1uNju01OrbXyNlWo7Nu3TpIQ49m7xxKkiRJkiQp7Sw6SZIkSZIkKe0sOkmSJEmSJCntLDpJkiRJkiQp7Sw6SZIkSZIkKe0sOkmSJEmSJCntLDpJkiRJkiQp7Sw6SZIkSZIkKe0sOkmSJEmSJCntLDpJkiRJkiQp7Sw6SZIkSZIkKe0sOkmSJEmSJCntLDpJkiRJkiQp7Sw6SZIkSZIkKe0sOkmSJEmSJCnt8rMdgCQNt3ZXE/2D8TEdozA/jysWT09TRJIkaaoYSx5i/iFJT5aWolMQBHOBzwNPBwaB24EPhGHYeop9XwP84KTNm8MwPC8dsUia2PoH4xxs7RnTMeZUl6QpGkmSNJWMJQ8x/5CkJxvz8LogCKLArUA5yaLTi4BVwHdO85QVwB1A/bCva8cahyRJkiRJknJHOno6rQYuAurDMDwCEATBe4A/B0FQfYreTiuBjUP7SpIkSZIkafJJR9FpD3DDSUWkROrf4lPsvxK4JQ3nlaQpw7muJElTgXMqSdLkMuaiUxiGTcCvT9r8fmDnyb2ZgiAoBhYBzw6C4O9JFqXuAG4Kw7DjbM4fi8UIw/BsnjopdXd3A9gmI2R7jU6m26usrIzW1m4aG580HdzojkM1DQ19dHV1pSmys5Ou9iorK+Po8W52j7FdFtVV01DUk/V2OR1/H0dnqrRXLBbLdgiSxpFzKknS5DLmOZ1OFgTBTcDLgfed6mEgCvQDNwLvIDkP1I/THYckSZIkSZKyJy2r1w0JguBjwKeA94ZhePvJj4dh+GgQBNPCMGxJbdoQBMEx4MEgCJaEYbhztOeMRqMEQTC2wCeRoTvetsnI2F6jMx7ttavrGHWnHJk7ctXVJcydW5umiM5eOttrMrXL6fj7ODpTpb02bNhgbydJkqQJKm09nYIg+ALwSWEEqWwAACAASURBVOAdYRh+6XT7DSs4Ddmc+nduumKRJEmSJElSdqWlp1MQBJ8C3g28MQzD7z3Ffq8EvgLMC8OwN7V5DRAHtqcjFkmSJEmSJGXfmItOQRCsBj4CfAa4MwiCWcMePg7UAP1hGLYBfyS5st23giD4JDAb+CrwnTAMD401FkmSJEmSJOWGdAyve3nqOB8GDp/0tQx4CPgiQBiGx4DnAnXAw8DNJFe+e0ca4pAkSZI0RVUWp3W6WklSGoz5kzkMw48CH32KXRaetP964JljPa8kSZIkDSmI5rF2VxP9g/Gzen5dZVGaI5IkeTtAkiRJ0qTQPxjnYGvPWT13ellhmqORJKVt9TpJkiRJkiRpiEUnSZIkSZIkpZ3D6yQph8XiCfY1dfHgnmY2H27jeGc/nb2DdPYN0Nk3SIQIJYVRSgujlBXms6i2jPPmVFFT6hABSZIkSdll0UmSckRn3yDbDrez9XA7Ww53sPVwO+GRDnoGYk/5vJ6BGM1dye/Dxg5+9dgR5taUsGpeNZedM51oXmQcopckSZKkJ7LoJEnjLJFI0NDSw5ZUgSn51cH+5u7TPqcoP4+6ymJmVhRRWVJAeVE+5UXJj/Ce/hjdAzGau/rYcriDrr5BGlp6aGjpYcOBVv5izTxmVLgijyRJkqTxZdFJkjJoMJ7gsYNtbDrYdqLAtO1wBx19g6d9zpzqEpbXV7KivoLl9ZUsr69kz/EuDrf1nvF8L16VYM/xLjYcaOWRfS00tPTw5bt28Lzz63npqjnpfGmSJOW0WDzBzqOdbD7UxmA8QUE0QkE0j5LCKOfPqWJmRXG2Q5SkSc+ikySlUe9AjAf3NHPvruM8sKORrUe76Rvcdcp9i/LzCGZVsHxWJctTBaZl9ZVUlRQ8ad99TafvBTVcXiTC4tpyFteWs3peNT9d10BbzwC3bjhEc2c/T1taS57D7SRJk1hTZx8P7m1mw4FWOnpPfZPn91uPsqi2jMvPmc7y+kqHoktShlh0kqTTWLurif7B+Bn3a+sZ4ME9TTza0EZ4pIP+2JOfU1tRxIpUr6UVs5O9mBZOLyM/mrlFRBfVlvOeZ5zLLx49yKMNbdyz8zjv/fF6XrFm3lkfszA/jysWT09jlJIkpc9De5v56p92MRBLnNh2zowyppUVMhCLMzAY50h7Ly3dA+w+1sXuY11MLyvk1ZfO54I5VVmMXJImJ4tOknQa/YNxDrb2nPaxLYfb2HCglZ1HO4k/ntsSAebUlFBbDBfOq+Kvn7mC+qqS8Qn6JCWFUV51yXxKCw+xdncTt208TEE0j4sXTjur482pzs7rkCTpqSQSCX704H5+8OB+ACqL87nknGmsnlfDtLInrugaTyTY0djJA3uaCI900NTVz/+9exd5EViz4Oyuj5KkU7PoJEmj0N4zwNrdTTy4p/kJq8oVF+SxfFYlS2dVsKS2nLKifBobj7CoriJrBafhnnd+Pf2xOOv2tXDLhoPUlBWyuLY822FJkjRmA7E4/++RBjY2tAGwcHopr7lswYkFN06WF4kQzKogmFXBodYefvjgfpq7+vnSH3bytHNn8MzldRRksCeyJE0lFp0kaQSOd/RxV3iUjQ1txBLJbk3RvAjLZlWwal41QV1FRofKjVU0L8JNzw3425sfpaG1hx8+sJ+3X7eYGeWuaidJmrgSiQQ3P3yAzYfaAXjaubU8a8VM8vNGdk2eXV3CO69bws8eaWDr4Xb+tOM4B1p6+MsrF1p4kqQ08JNUkp5CR+8At2w4yBd+v531B1qJJRKUFUZ55rKZ3HT9Ml572QJWzq7K6YLTkNLCfN7/7KWUF+XTMxDjlg0HSSQSZ36iJEk56oE9zScKTm+8YgFvvmrhiAtOQ0oKo7zusvn85RULAdhzvIufPnyAuNdISRozezpJ0in0DsT4+fqD3LHp8ImJwWtKC7gumMmqedUT9u7njPIiXn7RXL6zdi+7j3Wx9XA7K2Y7caokaeI53NbDHZsOA3Dh3CpecdFcmrsHzupYkUiEV6yZS89AjJ88fIDHDrVz26OHeNGFs4lEXNlOks6WRSdJOskj+1v48M82svNoJwClhVGeHszksnOmTYgeTWcSzKpgaV052xs7ueOxIyzN8aGBkiSdrH8wzo8fPMBgPMG0skJevGpOWopDzzu/noOtPfx553Ee2NNMRXE+z1hWl4aIJWlqsugkSSk9/TE+89uQb927h0QC8iJw9ZJargtqKS6IntUxa8qL0xxlejzv/Hp2Ht1Bc1c/9+1q4mlLa7MdkiRJI3bbxkMc6+wjGolw4yXzzvo6fSrXnzeLzr5BNhxo5XdbjzKjvIgL5lan7fiSNJVYdJIkYF9TF3/13YfZ3pjs3bS8vpJXXzJvzD2ACvOjrN3VRP9g/KyPUVeZ/sm+Z1YUc/mi6dy3q4m7wqOsnl9NRXFB2s8jSVK67WjsYN2+FgCeu7KOuTWlaT1+XiTCyy6aQ2fvIDuPdXLLhoPMm1ZKTWlhWs8jSVOBRSdJU96fdxznnT98hLaeAfLzIrzvWefy19cu5r6dTRxs7Rnz8fsH42M6zvSyzCS5z1xWx4YDrXT3x7hzSyMvu2huRs4jSVK6JBIJ7tzaCMDC6WVcuWRGRs6Tn5fHKy+ey5f+sJOuvkFufvgAb716EdE853eSpNFwEg9JU1YikeCbf97DG771AG09A0wvK+SHf3U573rGuRN2ovDRKCmM8qzlyXkq1u1r4WhHb5YjkiTpqYWNHTS0JG/kPGdFHXkZnOS7oriAV65J3pDZ19TN3duPZuxckjRZTf6/qiTpND7z25B/+uUW4gk4b04lv3j31Vx6zrRshzWuLlk4jWllhSSA+3c3ZzscSZJOK5FI8LtUL6dzZ5azcEZZxs+5tK6CKxdPB+AP246yr6kr4+eUpMnEopOkKem//7iL/7prFwDPP7+en/71lcypLslyVOMvmhfh8lSh7ZH9LfQOxLIckSRJp7b1cAeHWpO9cod66o6H566cxazKYuIJuPnhA2Oap1GSphqLTpKmnO/dv4//8+ttAFy/chZfvHEVJYXpW/VmolmzYBoF0Qj9g3Ee2d+S7XAkSXqS+LBeTkFdBfOmpXfy8KdSEM3jVZfMIz8vQkv3AH/Y1jhu55akic6ik6Qp5efrG/j4rY8BcM25M/jiq1eNeYW6ia6kMMqqeTUA3L+7iXgikeWIJGVKEARXBkGQOOmrM9txSWey+VA7R9rHv5fTkLrKYq5dWgvAn3ce50ib8yBK0khM7b+0JE0pjx1s48M/20giAZcsrOGrr19DUf7U7eE03BWLkvNVHO/sZ9dR//6UJrEVwCagftjXoqxGJJ1BIpHgrm3JSbyX11cypyY7w+GvXVrLjPJC4gm4ZcNBb9JI0ghYdJI0JfT0x3jvj9czEEuwqLaMb/7lJZQW5mc7rJwxq6qYc1ITsq7d3ZTlaCRl0EpgcxiGR4Z9uSSXclpDS8+JXk5PD2qzFkd+NI8Xr5oDwP7mbh7e65B0SToTi06SpoRP/2oru451kZ8X4Us3rqayuCDbIeWcod5O4ZEOmrv6sxyNpAxZCWzLdhDSaKzblyzuzKoszvqiH4try1k9rxqAX28+TEfvQFbjkaRcZ9FJ0qR317ajfHftPgD+9jlLOW9OVZYjyk3L6yupKikgQXJuJ0mT0krgwiAIHg2CoCEIgh8EQTAr20FJp9M/GOfRhlYA1iyoIRKJZDkiuOH8ekoKovQOxPn1Y0eyHY4k5TTHlkia1Jo6+/jQzzYCcOnCafz10xZnOaLcFc2LcNk50/jtlkbWH2jluStnEc3LfnIvKT2CIKgCZgMFwFuBMuBfgV8HQXBJGIaj6rIRi8UIwzD9gWZJd3c3wKR6TZmUifYqKyujtbWbxsbWE9u2HeulbzBOXgTmFPfT2Hj6Ik9ndZTOzn4aG4+d1flH8/wr55fw+12drD/QytJqmFVRQBnVNDT00dXV9YR9fW+Nju01OrbXyNlW2WFPJ0mT2qd+uYXjnX1UFOXz2b+40CLKGVyYGjLQ1TfI3qauM+wtaSIJw7ANqAJeEobhQ2EY/hF4KXA+cG02Y5NOZ/PR5FxOi2oKKSnInT9dVswsprYsef/+j3s6STipuCSdkj2dJE1aWw61c+uGQwB89AXLmTetNMsR5b6a0kLm1ZRwoKWHTQ1tLK4tz3ZIktIoDMP2k35uDIKgCZg72mNFo1GCIEhbbNk2dOd7Mr2mTMpUe+3qOkYdxQC0dPXT0JbsdXRlUE9dXeVTPre8rJw++qmrO7uVaUf7/JfkV/L1e3bT2DnIob5i1lTXMHfukyc69701OrbX6NheI2dbjc66devScpzcuV0gSWn2uTuTF5YlM8t5xZp5WY5m4jg/NefVY4faiMW9cytNFkEQXB4EQUcQBPOHbZsPzAC2Zi8y6dTW7U9OIF5RnM+5MyuyHM2TnTOjjAvmJq+Zv9l8hJ6BWJYjkqTcY9FJ0qT0yP4Wfrc1uQr4B5691GF1ozA00Xp3f4zdxzuzHI2kNFoPHAK+FQTBBUEQXArcDPwxDMMHshua9ETxRIJHUkWn1fNqcvY6fv3KWRREI3T0DXLbo4eyHY4k5RyLTpImpc/8JtnL6fw5VVx/ngszjUZ1aSHzU0MRNzW0ZTkaSekShmEfcAPQCdwN/BbYDrwim3FJp7L7WBet3cm57dcsqMlyNKdXXVrI05Ymh9T9ZvMR9h53PkRJGs45nSRNOlsOtXPfriYAPvCcpTmxvPJEc/6cKvY3d7P5UDsvXpXI2TvMkkYnDMPdwEuyHYd0JhsbkivYzZ9WSm1FUZajeWrXLKll3d4WWnsG+Jc7tvL1N1yc7ZAkKWfY00nSpJJIJPjZIwcAuHThNK5d+uQJPXVmQ0PsegZi7DrmEDtJ0viJJxJsO9IBPH49ymWF+XnccH49AHduaeSeHceyHJEk5Q6LTpImlR1HO9l1LNm1/YPPDezldJaqSgpYMN0hdpKk8XewpYfOvkEAls/KvQnET+W82ZUEdclYP3XbFgZi8SxHJEm5waKTpEnlwT3NAFy+aBqXnjMty9FMbEOr2G0+3MZg3ORZkjQ+th5uB6C2oojp5bk9tG5IJBLhtZfNJy+SvAH2/fv3ZTskScoJFp0kTRrtvQNsO5JMVF9z2YIsRzPxnTenigjQOxBn11GH2EmSxsfW1LV8+azKLEcyOguml3HjpfMB+Pyd22nu6s9yRJKUfRadJE0aj+xrIZ6A8qJ8nruyLtvhTHiVxQXMTw2xG5pbQ5KkTDrW0Udjex8Ay+snxtC64T7w7KVUFOfT3jvI5+4Msx2OJGWdRSdJk0I8keChvcmhdVcvmUFRfjTLEU0OQ/NTbG/sIJFIZDkaSdJkt/5ACwClhVHmTSvNcjSjN728iPc/aykAP3xgP1sOtWc5IknKrvxsByBJ6bD7WBct3QMArliXRkvrKvjtlkZaugc43tnP3JqJ9weAJGni2HCgFYBlsyrJm6CLgbz+igX84IF97DrWxad+uZlPPK1mxAubrN3VRP/g6OdRLMzP44rF00f9PEnKNItOkiaFB1O9nBZML2V2dUmWo5k86quKqSjKp6NvkO2NHayaV53tkCRJk1R77wDbDieHcy+bIKvWnUpBNI+Pv3Alb/zWg9y/u5k/LyjkmoXlI3pu/2Ccg609oz7nHHMfSTnK4XWSJrzOvkG2prqvX7rQFevSKRKJcG5dMlHecdR5nSRJmfOn7ceIJRJE8x6/9kxU1y6t5VnLZwLw9YeO03cWvZckaTKw6CRpwlu/v4VYIkFxQR7nzanKdjiTztLUvE67j3WdVZd/SZJG4ndbGgFYXFs2KeZm/MjzV1AQjdDYOcjPHmvNdjiSlBUWnSRNaIlEgof2JicdXT2vhoKoH2vptqS2nAgwGE+w7YgTokqS0m8wFueu8BiQnM9pMjhnRhlvvvocAH6ysYVDZzFsTpImOv86kzShNbb3cbwzubTymgU1WY5mciotymduTXKuiE0H27IcjSRpMtpwoJW2nuSCIBN5PqeTvevpS6gpidIXS/B/fr0t2+FI0riz6CRpQttyOFkEqSktoL6qOMvRTF5DQ+wsOkmSMmHtriYguYBFdWlhlqNJn4riAt68Jrmq3K0bDvFwauETSZoqLDpJmtC2HE4O91pRXzni5Yg1ekNFp8NtvRxo7s5yNJKkyWbt7mTRaXn95BhaN9yzllSwdEYRAJ+4bTPxeCLLEUnS+LHoJGnCau3u51BrLwDLZz+epFYW52crpElrTk0JpYXJSV3v3n4sy9FIkiaT3oEY6/Yl52dcPknmcxouLxLh7ZfNAOCxg+38dN2BLEckSePHv8wkTVhDvZxKC6MsmFZ2YntBNI+1u5rGtNJaXWXRmOObTPIiEZbMLGdjQxt/DI/xussXZDskSdIksX5/K32pa/ay+go6egezHFH6rZhZwktXz+Hn6w/yH78JueH8eiqLC7IdliRlnEUnSRPWUNFp+axKonlPHFrXPxjn4BhWiZleNnnmk0iXoK6CjQ1t3LfrOP2DcQrz7SwrSRq7oaF1y2ZVUFlcMCmLTgA3Xb+M32w+wvHOfr78+x185Pkrsh2SJGWcfzFImpC6+wbZe7wLgBWzJ19X/Fy0ZGY5AN39MTYdbM1yNJKkyeL+1CTily+anuVIMmtWVTHvfPoSAP7n3r3sPNqZ5YgkKfMsOkmakLY1dhBPQEE0cqIYosyqKH58hcD7d7v6jiRp7Hr6Y6w/kJzP6YrFk7voBPCWq89h3rQSBuMJPvGLzSQSTiouaXKz6CRpQtpyKDm07tyZFRRE/SgbL8tSE7zenxoKIUnSWKzb18JALEEkApefM/mLTsUFUT7xwpUA/HnncX712JEsRyRJmeVfapImnP7BODuOdgAOrRtvy2dVAPDw3pYxTdQuSRLAfbuOA7CivpKq0qkxsfYzl9fxzGUzAfinX26hq29yzmElSWDRSdIEtOtYJwOxBHmR5KSjGj9Bqr17BpzXSZI0dkOTiF85BYbWDfePL1xJYX4eh9t6+c+7dmY7HEnKGItOkiacralV6xZOL6O00EU4x1N1aSGLassA53WSJI1NZ98gGxvagKkxn9Nw86eX8vZrFwPwjXt2O6m4pEkrLUWnIAjmBkHw0yAIjgdBcCQIgm8GQVB9mn0rgyD4XhAEral9Px4EQeRU+0rSqew6lkzMltbZyykbhlYXcl4nSdJYPLS3mVg8QTQvwiULp2U7nHH39usWM29aCQOxBJ+8zUnFJU1OYy46BUEQBW4FyoGnAy8CVgHfOc1TvgEsAa4D/gp4H/DOscYhaWpo7uqnpXsAgMW1rlqXDUNFp4f3tjAQc14nSdLZuX9X8ubFeXOqqCieGvM5DVdcEOXjL0hOKn7PjuPcFR7NckSSlH7p6Om0GrgIeFMYhpvCMHwQeA/wopN7OwVBsAB4BfC2MAw3hGF4G/CPwPvTEIekKWCol1NJQZT66uIsRzM1XX5O8m50z0DsxLAISZJGa6jH7BWLptbQuuGetXwmVy1Jvv5/vn0rg3Fv5kiaXNJRdNoD3BCG4fD1Pof6hp78F+EVQFsYhpuGbbsbWBQEQX0aYpE0yQ0Vnc6ZUUZexJG52TCzsnjYvE4OsZMkjV5Pf4zNh5JzNF52ztQbWjckEonw0eevIC8Cu491cdc2eztJmlzGPANvGIZNwK9P2vx+YOdJhSiAOcDBk7YdTv07d9j3IxaLxQjDcLRPm7S6u7sBbJMRsr1GJ9PtVVZWRmtrN42Np14VLZFIsLMxmaDOLI7T2HjyR0xSZ3WUzs5+GhuPnXUs6ThGf38/sViMzp7OrMeSruOUUU1DQx/LpkXZfQz+sGk/z5odG1NcQ/x9HJ2p0l6xWHreX5Jyy8aGVgbjyfvUq+efcirYKWN5fSWvumQ+P3pwPz9ff5CFM1woRdLkkfbV64IguAl4Ocm5mk5WCvSdtG3o56J0xyJpcmnuidE9kExQ51VNvbkfcskFs0oAeOxo74k/GiRJGql1+1sAWFxbRnVpYZajyb4PPGcp5UX5dPXH+IO9nSRNImktoQdB8DHgU8B7wzC8/RS79PDk4tLQz91nc85oNEoQBGfz1Elp6I63bTIyttfojEd77eo6Rt2TRuamHtt1HICK4nyWLZxD5DTD68rLyumjn7q66FnHkY5jNDYeIRqNUl5ekvVY0nWc6uoS5s6t5aWVM/i3uxvpG0zQW1rHmgU1Y4oN/H0cranSXhs2bLC3kzQJPbIv2as5HdePyWBGeRHvesYS/u1X27h/dxOXnTOd2grvyUua+NLW0ykIgi8AnwTeEYbhl06zWwNw8txNQz8fSlcskianXce6gOSqdacrOGl81FUWs2iG8zpJkkYvkUjwSKqn00XzLToNedNVC6ktLyKegN9va8x2OJKUFmkpOgVB8Cng3cAbwzD876fY9T5gWhAEK4ZtuxbYfYr5nyTphFg8wZ7jyUnEF6cmsVZ2XZZabeiBPc1ZjkSSNJHsa+qmuasfsKfTcEX5UV6yeg4AmxraONrem+WIJGnsxlx0CoJgNfAR4DPAnUEQzBr2lR8EQW0QBFUAYRjuB24BvhsEwZogCF5AsnfUZ8Yah6TJ7VBrD70DyWWEF9WWZzkaAVyyMPmHwvp9LcSc10mSNELr9iV7OVUW57PYa/oTXLFoOtPLCkkAfwid20nSxJeOnk4vTx3nwyRXnxv+tQx4CPjisP3fDOwC/gR8HfjcGXpHSRK7jyV7OU0rK6TGCUdzwsULkktcd/QNsr2xI8vRSJImiqFJxFfPryEvz+Hyw0XzIjx92UzA3k6SJocxTyQehuFHgY8+xS4LT9q/BXjVWM8raWoZPp+TcsO8aSXMrCjiaEcfD+9tZnl9ZbZDkiRl2dpdTfQPxp9yn3u2HwOSN5LuDo+d2F5X6cTZABfOreaubUdp6urnD+FRbrxkfrZDkqSzlraJxCUpUwZjcfY2DRWdnM8pV0QiES5ODbF7ODVUQpI0tfUPxjnY2nPar13HOmlo6QGgprTwCY8NxhyqDfZ2kjS5WHSSlPMOtPQwmJozyPmccsua1BC7h/dadJIknVlDSw8JIALMrSnJdjg568K51c7tJGlSsOgkKeftT/Vyqi0vorxozKOClUZDk4kfbO3hcFtPlqORJOW6fc3Ja3pdZTHFBdEsR5O7Tu7tNLTanyRNNBadJOW8fc3dAMyfXprlSHSy5fWVlKT+aLC3kyTpTPY3eU0fqQvnVlNVUkACeGBPU7bDkaSzYtFJUk5LJBLsTxWdFkwzQc01BdE8Vs2rBh5fAluSpFOJJxIcaEkVnbymn1E0L8Kl5zw+jP1ME7RLUi6y6CQppzV19tPdHwNMUHPV45OJN2c5EklSLjvW0UfvQLJw4o2kkblk4TSieRF6BmJsbGjNdjiSNGoWnSTltKGhdSUFUWZUuJRyLrp4YfIu7JZD7XT2DWY5GklSrhrquVxaGGVaWWGWo5kYyovyOX9OFQD3724ikXCFP0kTi0UnSTltf2rC0fnTSsmLRLIcjU5l9fxqIhGIJ2DDfu/CSpJOrWHY0LqI1/QRu2LRdAAOtfVyIFW4k6SJwqKTpJy230nEc15lcQFBXQXgEDtJ0uk1tCRXOZ1TU5LlSCaWuTUlzKlOttna3U4oLmlisegkKWf19Mc42t4HOJ9Trjsxr5Mr2EmSTqF/ME5jey8A82q8po9GJBLh8lRvp8cOttPRO5DliCRp5Cw6ScpZB1q6SQB5keRdPuWuS1LzOq3f38JgzNV1JElPdLith3hqOqKhXjsauQvmVlFaGCWWSPDQXnsVS5o4LDpJyllDQ+tmVRVTlB/NcjR6KmsWJHs6dfXH2HakI8vRSJJyzdDQuprSAsqK8rMczcRTEM07ca19ZH+rE4pLmjAsOknKWfubHp9wVLltTnUJsyqLAXhkv0PsJElPNDSJ+FyH1p211fOTRafmrv4TRTxJynUWnSTlpHgiwf4Tq9yUZTkanUkkEjlxB3bdPotOkqQnGiqSOFz+7M2qLD5xg+fRBleLlTQxWHSSlJMa23vpH0zODbTAnk4TwkUWnSRJp9DTH6Opqx+wp9NYXTi3CoCNDW3EHWInaQKw6CQpJ+1LDa2rKM6nurQgy9FoJC6aXw0k72YfTa1QJEnSwdZkL6cIMLu6OLvBTHAXzE1eazv7Btl9rCvL0UjSmVl0kpSThiYRnz+tlEgkkuVoNBIrZ1dRmJ+8rDivk5TbgiD41yAI9mY7Dk0NQ/M51VYUuTDIGNWUFZ7oAf7oAYfYScp9Fp0k5aThRSdNDIX5eSe6/TvETspdQRCsAT6U7Tg0dQzN5zTPoXVpceG8ZG+nxw61MRCLZzkaSXpqFp0k5ZzuvkGaU3M/WHSaWC6a//hyzpJyTxAEhcC3gfuyHIqmkKGeTnOcRDwtzptTRV4E+gbjhEc6sh2OJD0li06Scs7wuR/qq0xQJ5KhycQ3NbTRNxjLcjSSTuHjwE7gp9kORFNDe88A7b2DwORfua6yOP+U28vKyigrS99KvOVF+SyZWQ64ip2k3HfqT0ZJyqKGVNGprrL4xBxBmhiGejr1x+I8drCdNakilKTsC4LgIuBtwAXAK8Z6vFgsRhiGY44rV3R3J3vjTKbXlEmnaq+ysjJaW7tpbHy8ELKruQ+AaATyettobGw/7TE7q6N0dvbT2HjsrGLK9vMXVUf5/aYO/j979x0l13neef57K3WozjkBaATiIhMECAaRFCnLSlawZUmmZGsk2WNbtteW1+sws8eWvWPveHb2yLNjWT6eI9myZVmUJcrKwRIpkmAQSCQiAxdAo9HogK7OOVRX2D9uVQMEETpU1Vvh9zmHh+xC1a0fLru7qp77vM87NfPazTQWIm7Rzd97/raPX1NXwdTU7JKef32FxfkQnLs6wZXePoLU0NMzz/R07g8X18/i8uh8LZ3OlRkqLtOLAwAAIABJREFUOolI1knOflAbfu6pLy9iXW0pXcMzvHplVEUnkSxx3bK6P3Qcp9+2bcOJpFCEptyCS13Qh9eT/xuDzIWjXAq9tvsoHHZHBgQCgds+trFq6d1QG2oC+DwQiUHnaJitrcvPKiKSCSo6iUjW6U3Mfsj3Nvx8tWdtNV3DMxzpGuVXHzGdRkQSPgn0Oo7zhVQd0Ov1kk/Fq+SV73z6O6XTrc5Xx/QgjRQvfj12sROA9Q0VNDY23faYZcEy5gnT2LiyHe6y9fGhUD9Ayv/+dzUucPbqBH0zHqqqqmlrq19R7myjn8Xl0flaOp2r5Tly5EhKjqN1KyKSVUZnwtdmP1RpiHguSs51OtI1SjweN5xGRBI+DLzJtu0p27angL8C1ia+VnlY0iIejy92L+s1PfW2NJUDcD40SSSmXexEJDup6CQiWaVz0J1F4PVYNFYWGU4jK7E3MddpYHJ+cSi8iBj3GLAD2J34578BfYn/PmwuluSzkekwswvuphJaMp96dqLoNB+Jcb5/ynAaEZGb0/I6EckqncNu0am5shifR3XxXGQ3lRMMeJkORznSNUpbta5ui5jmOE7X9V/btj0ERBzHuWgokhSA5IWHgNdDfbkuJKVaRbGftuoSekZnebV71HQcEZGb0ic6EckqnUNu0am1SldEc5XXY7F7bRUAR7v0JlhEpFAli07NVcV4rPwfIm5CstvpWPeYlrSLSFZS0UlEskY8HufSkNseru6Y3JZcYnf0ytgd7ikiJjiO8xnHcdpN55D8liw6telCUtpsbaoA3CXtHYkRBSIi2URFJxHJGt0js0zPu7MftHNdbrsnMUz8zNUJZsIRw2lERCTT4vE4fYmiU4uKTmnTXFlMRbE7MeXHZ0OG04iIvJ6KTiKSNY73uF0xmv2Q+/ascYtO0Vic493jhtOIiEimjUyHmVtwd1TTkvn0sSyLLYlupx+fHTCcRkTk9VR0EpGscSJRdGrR7IecV1nq566GMgCOXtFcJxGRQrM4RNznoU4XktJqS7M71+lw1whjM2HDaUREXktFJxHJGid63I4YzXPKD3uSc500TFxEpOD0jiaW1lXqQlK6bawvI+D1EIvDc86g6TgiIq+hopOIZIVoLM6pXrfo1Kp5Tnlhb2Ku05Ero9pRR0SkwCQ7nbS0Lv38Xg/bWtwldk9rrpOIZBkVnUQkK1wanGI6nBgirjeoeWFPoug0NrPApSHtqCMiUihi8fi1opMuJGXEPWuqANh/fpBINGY4jYjINSo6iUhWOJ5YWhcs8lITDBhOI6mwoS5IVakf0BI7EZFCMjIdZj7iFj60c11m7GytBGByLsLJXm3gISLZQ0UnEckKyaV17bVBLM1+yAsej7V45VXDxEVECsdrhoiXaYh4JtSWFbGhLgjASxeHDKcREblGRScRyQrXF50ke1UU+5Z1/8W5Tup0EhEpGNeGiJdoiHgGPbSpDoAXVXQSkSyyvE8PIiJpEIvFOXN1AoB1tdq5Lpv5vR4OdAwTjixtXoTP417buBCa4vsnrhIscl92Aj4PD26sTVtOEREx59oQ8WLDSQrLQ5vq+OLLXRztGmMmHKE0oI96ImKefhOJiHGdw9PMJIaIt9cGicS001k2C0diix8o7qTI78EC4sDByyNsbiwHtJuRiEi+isXj9C0OEdeFpEx6cEMtHgvC0RiHLo/y6OZ605FERLS8TkTMSy6tKy/yUV+u2Q/5pMjnpbnSvdJ9ZWTGcBoREUm3gYm5xSHiusCQWZWlfna2ubMUNddJRLKFik4iYtzpPndp3daWCs1+yENrE0smVXQSEcl/ncPu7/oin4faMu1Gm2kPb3KXrr94QUUnEckOKjqJiHGn+9xOpx0tlYaTSDqsrXGLTt0jM8TiWjopIpLPLg9NA9BSpSHiJiSHiZ+5OsHw1LzhNCIiKjqJiGHxeJxTvW6n047WCsNpJB3W1rg7Es5HYoQm5gynERGRdLo87BadtLTOjD1rqyn2ux/xftIxbDiNiIiKTiJiWM/oLOOzCwDsaFWnUz6qLvVTnti1TkvsRETyVywWV9HJsGK/l33tNYDmOolIdlDRSUSMSi6tK/J52FAXNJxG0sGyrGtznYZVdBIRyVedw9PMLWiIuGkPJ5bYvXBhiLiWtYuIYSo6iYhRi0PEmyvwefUrKV+tS8x1Sl4BFxGR/JPcjbbI56FGQ8SNSc516h2bVYexiBinT3giYlTyDarmOeW39kQX2+jMwuJyShERyS8netzXdA0RN2tbcwXVpX4AXtQSOxExTEUnETHqVKLTSTvX5bfmyhL8XvcDSJe6nURE8tLJxIUkLa0zy+OxeMNGt9tJw8RFxDQVnUTEmIGJOQYn3e18t6volNe8Hos1i0vs1OovIpJvYrE4p5NFp2oVnUx7YIM7TPxg54jmOomIUSo6iYgxyXlOPo/F5qYyw2kk3dpr3SV26nQSEck/l4ammQ5HAXU6ZYP7N9QCMDg5T+eQXndFxBwVnUTEmOQ8p82N5RT5vIbTSLoli07943PMhCOG04iISColX9NL/F5qghoibtqm+rLFuU4HO0cMpxGRQqaik4gYc6rPfYO6vUVDxAvBmpoSPBbEgQsDU6bjiIhICiWHiLfXlmqIeBbweCzuW+8usXtFRScRMUhFJxEx5lRvYoh4q+Y5FYIin5fmSnfJxYXQpOE0IiKSSslOp+RupWLefevdJXbqdBIRk1R0EhEjxmbC9I7NArCjVZ1OhaK91h0mfj6kTicRkXwRjcU5neheXl+rolO2uD/R6dQ7Nkv3iDbxEBEzVHQSESOSQ8QtC7Y2q+hUKNYlPoxcGppiPhI1nEZERFKhc2hqcYi4Op2yx9bmCsqLfYCW2ImIOSo6iYgRyTb8DXVBSgM+w2kkU9YlOp0WovHF7wEREcltJxO/z8uLfTSUFxlOI0lej8W+drfb6WDnsOE0IlKoVHQSESNO9WmeUyEqL/ZTm9jV6GDnqOE0IiKSCskh4jtbK7E0RDyraJi4iJimopOIGJGc/bCjRUWnQpNcenH4st4Ai4jkg2Tn6k5dSMo6yblOXcMz9I/PGU4jIoUopWtabNu2gO8D33Mc5zO3uE8L0HuTP6p3HGcolXlEJDtNzUfoHJoGYLuGiBec9tpSjnSNcrhrlFgsjsejq+IiIrnKHSLudi/vbFPRKdvsaK2kNOBlJhzllc5hfnZ3q+lIIlJgUtbpZNu2B/gb4O13uOs2YAxovuEfLTQWKRBnr04Qj7v/vb1Zb1ALTXKY+PjsAucHJg2nERGR1bg0OMVMYoi4Op2yj9/rYe+6agAOaomdiBiQkk4n27Y3AF8A1uAWlG5nO3DOcZz+VDy3iOSeZBv+mpoSKkv9htNIptUGA1SV+BmbXeCVSyNsaVK3m4hIrkrOc6oo9rG2ppTLQzOGE8mN7l9fwwsXhjTXSUSMSFWn0wPASWAPcKftiLYD51L0vCKSg5Jt+JrnVJgsy2JLczkABzrU5CoikstO9LjXm3e1VWmIeJa6b30tABcHphiamjecRkQKTUo6nRzHeQJ4AsC27TvdfTswbdv2K8Ba4DDwe47jXFzJc0ejURzHWclD89LMjHt1SedkaXS+lidV5+vIpQEAGgPh1xwrGAwyNjZDKHSnhsnbm6ryMjUVJhQaNHqMcDhMNBplanbKeJZUHSdVWdZV+HgZ+MnFAc6eO4fHsvTzuEyFcr6i0ajpCCJyG8cTnU67NM8pa929ppKAz0M4EuNQ5wjv2NlsOpKIFBATu9dtBcqB3wfeC8SB/bZtVxnIIiIZFo7E6BoLA7CptshwGjFlU30JABPzMS6Phg2nERGRlViIxjlz1e1e3tWmt/ImVRTfupegyOflnjXu/x8tsRORTEvp7nVL1AZEHceZB7Bt+3HgCvB+4O+XezCv17uU7qqCkbzirXOyNDpfy5OK83W8e4xY/BIAb7tvO/Xlry08dUwP0kjxykMCZcEy5gnT2Og1eoxQqB+v10tZWYnxLKk6TqqytFQW01QRon9ijqvRct5hr9fP4zIVyvk6duyYup1EstTl0XnCkRjgdtOIOX6vhwMdw4v/P27UVOm+t3rm3ABvshte9+cBn4cHN9amNaOIFKaMF50cx5m54etZ27Y7cYtRIpLnkvOcGiuKXldwksJhWRYPbKjhm8f6ePnSML/y8HrTkUREZJnOD7nzgerLi2iqWN0FI1m9cCRG79jsTf+sNui+5+oemeHiwBQlgddePGqtKkl7PhEpTBldXmfbdqtt2+O2bT9y3W3lwGbgbCaziIgZp/rc2Q8aIi7JK6qvdI4Qi8UNpxERkeVKFp3ubqvUEPEst7amFK9lEQcuD0+bjiMiBSTtRSfbtitt264HcBynFzgKfNq27ftt294FfBnoB76e7iwiYt7pXrfotL2lwnASMe3BDXUAjM8ucLZ/wnAaERFZLmdoDtA8p1wQ8HlorXa7mTqHVHQSkczJRKfTXwOHrvv6F4ATwHeAA0AUeJvjOAsZyCIiBi1EY5ztnwRge6s6nQrdmpoSWhIzJg50DBtOIyIiyzF33cYg2rkuN6yvCwLqdBKRzEr5TCfHcdpv+PpjN3w9CHw01c8rItmvY3BqccDlDhWdCp5lWTywsZavH+3l5UsjPNJQbjqSiIgsUcfwPMmV0ep0yg3r64LsPz9I39gs8wtRivyr2xRERGQpMjrTSUQK26ledwlVdal/scNFCtuDG5JznYaJaq6TiEjOcBLznNbUlFATDBhOI0uxtqYUC4jFoWtk5o73FxFJBRWdRCRjTi3Oc9LAUXE9kCg6Tc5FuDQybziNiIgs1fnEPKe71eWUM4r9XlqqNNdJRDJLRScRyZjTfRoiLq+1pqaUtsRg0+P9N9/mWUREss+1netUdMolyblOKjqJSKao6CQiGRGLxTnd5y6v0zwnuV6y2+n4VRWdRERywfjMAr0T7h5AGiKeW5JFp97R2cU5myIi6aSik4hkROfwNDPhKKCik7zWGza6RaeT/bNENNdJRCTrnegdA8Bj6TU916yrdec6ReNxukc110lE0k9FJxHJiOQ8p7IiH+tqSg2nkWzy8KY6AGYjcc4OzBlOIyIid3Kix31NX1MZIFiU8s2wJY1KAz4aK9zNXLTETkQyQUUnEcmI5NK6bS0VeDwaIi7XNFQUs6WpHIAjvbrqKiKS7Y53u51Om+uKDCeRldBcJxHJJBWdRCQjkp1OO9WGLzfxxs31ABzpU9FJRCTbJTud7Lpiw0lkJdoTRafukRkiUc11EpH0Uj+siKRdPB5fLDrtaNXOdfJ6j9xVx2efv8SFoXlGp8NUBwOmI4nkJdu2NwOfBh4CpoB/Aj7pOE7EZC7JHVfHZ+mfcJdCq9MpNyU7nSKxOD2js4tFKBGRdFCnk4ikXffILBNz7ueZHS3qdJLX29deQ8BrEQde6hgyHUckL9m27Qf+HRgF9gIfAj4MfNJkLsktx664S+sCXosNNSo65aKyIh/1Ze7/u85hLbETkfRS0UlE0u5Un9vlVOz3sKG+zHAayUbFfi87G91lGi+cV9FJJE1agUPAxx3HOe84znPAk8CbjKaSnPJqYp7TXbVF+L2a0Zirkt1OlzXXSUTSTMvrRCTtkkvrtjVX4NUQcbmFPa2lHOmb5YULg8TjcSxL3ysiqeQ4zmXg8eTXtm3vAd4LfMFUJsk9r14ZBWBLveY55bL2uiAHL4/QNTxDNBY3HUdE8pg6nUQk7U4ldq7boSHicht7W0oB6Bufo2NwynAakfxm2/Zp4AgwDHzKcBzJEQvR2OIQ8a0NKjrlsmSnUzgao29s1nAaEcln6nQSkbSKx+OcTg4R1zwnuY326gA1JV5GZqM8f36ITQ3lpiOJ5LP/ANTgDhX/OvDW5R4gGo3iOE6qcxkzM+PunplPf6dUuzA0x3zE3e1sXVmcmZmZ15yvYDDI2NgModDYio4/VeVlaipMKDSYV48Ph8MAhEL9aXv+lTy2stjD+FyMk5f72VLjoadnnulp88vt9LO4PDpfS6dzZYY6nUQkrfon5hiedt9sqdNJbseyLPYkup1euLCyN/wisjSO4xx1HOdp4GPAW2zb3m44kuSAs4PurnV1pV7qSr2G08hqtVa4O8X2TiwYTiIi+UydTiKSVqd63aV1Aa+Huxo1RFxub29rKU93TPLypRHmI1GKfPpQI5Iqtm23Afc5jvP1624+lfh3/XKP5/V6sW07JdmyQfLKdz79nVKt79gxAO7bWE9pqXuR4Mbz1TE9SCMrW3pXFixjnjCNjSv73Z+tj092ODU2NqXt+Vfy2G1zRZwZ6OHqVISKiira2hqW/bzpoJ/F5dH5Wjqdq+U5cuRISo6jTicRSavkEPEtzeX4vfqVI66K4tdf8wgGgzy8qRaA2YUoR7pGMx1LJN/ZwNcSxaekfUAcOGsmkuSS5BDxe9ZUG04iqdCemOs0txCje3TGcBoRyVfqdBKRtEoWnbZrnpNcx+/1cKBjmHBiNgjA2Jj7hnddTSldIzP8y4EuFiK331En4PPw4MbatGYVySPPA8eBf7Zt+xO43U2fBT7rOE7IaDLJeiPTYS4Pu7+n71lbBfNaBp3rqkv9VJb4GZ9dwOmfNB1HRPKUik4iklan+hJDxFsrDCeRbBOOxOi9bsec5ODZ9fVBukZmONQ1ysN33X7FT2tVSVoziuQTx3EWbNt+N/DXwIvAAvAl4D8ZDSY54Vi32+Xk81jsaK2k65KKTrnOsizW1wU51j3GORWdRCRNVHQSkbQZmJwjNDEPaOc6WbqtTRU85wwyODnP8NQ8tWVFpiOJ5A3HcXqA95nOIbnn1SvuhYFtLRUU+zVvL1+sr3WLTk5okng8jmVZpiOJSJ7RgBURSZvTfe4Qca/Hwm4qN5xGckVrdQnBgPuBRldeRUSyQ7LodM+aKsNJJJWSc52m5iNcGJgynEZE8pGKTiKSNqcT85zuaijTVVFZMo9lYTe5yzE1Y0JExLxoLM6x7kTRaa2GiOeTurIAZUXu4pdXLg0bTiMi+UhFJxFJm1O9bqfTzlYtrZPl2ZLojOscmmZuIWo4jYhIYesYnGJqPgIkhohL3kjOdQL4SYeKTiKSeio6iUjaXBsirqKTLM9dDWV4PRbReFzt/iIihr16xR0iXhMMsLam1HAaSbVNDWWAW3SKxm6/a6yIyHKp6CQiaTE2E6Zn1N2ZTDvXyXIV+b1sSFx5PXd1wnAaEZHCdrTLXVq3e02VBk3noY31btFpfHaBM316zRWR1FLRSUTSIrm0zrJga7OKTrJ8yeHzTmiSWFxXXkVETDncNQLA3nWa55SPaoIB6svdnWJfvDhkOI2I5BsVnUQkLZJL6zbWl1Ea8BlOI7loS2KY+Ew4SvfIjOE0IiKFaXhqno7BaQD2tdcYTiPpsi1xgfAnHSo6iUhqqegkImlxKrFz3Y4WdTnJytQEAzQkrrye0y52IiJGHOly5zkFvB52tWlGY77anni/drBzRBt4iEhKqegkImlxOjETQEPEZTWS3U7n+jVjQkTEhMOJotPOtkqK/V7DaSRdkqMQ5iMxjiYGx4uIpIKKTiKScpNzC3QOua34KjrJamxJzHUKTcwzMh02nEZEpPAcuuzOc7q3XfOc8llFsX9xid1LmuskIimkopOIpNz1O59s0/I6WYW1taUEi9yZYKcTc8JERCQzZsPRxeXy+9ZpnlO+e2hTLQAvXRw2nERE8omKTiKScqcSRaf22lIqiv2G00gu81jW4pXX09rGWUQko473jLEQdXcP1c51+e+hTXUAnOgZY3x2wXAaEckXKjqJSMqdTlwV3a6ldZICyWH0V0ZmmNCbYBGRjEkOEd/UUEZ1MGA4jaTbfetr8HstYnF45ZK6nUQkNbSPuYikxIGOYcKRGAAvd7rzH4J+L/udwSUfo7GiKC3ZJLetrw9S7PcwtxDj9NUJHtxQazqSiEhBSM5z2qd5TgWhNODjnrXVHOwc4aWLQ7x1e5PpSCKSB1R0EpGUCEdi9I7NEo7EuDo2C0Cw2Edv4r+XolZXUeUmfB4PW5sqeLV7jNO94yo6iYhkQDQWX+x0ulfznArGQxvrONg5wosaJi4iKaLldSKSUv3js8QT/91SWWI0i+SP5C6InUPTTM9HDKcREcl/50OTTM65v2/3tavoVCgevsu9sNMxOM3V8aVfOBQRuRUVnUQkpXrH5wCoKvEv7jomslqbGsoI+DzEgbNXNVBcRCTdDieW1jWUF7GmRheRCsXdbVVUFLvv3549t/QRCSIit6Kik4ikVF9iOV1Lld6gSur4vR7sxnIATvWNG04jIpL/Dl12l9bta6/BsizDaSRTfF4Pj9oNADxzLmQ4jYjkAxWdRCSlVHSSdNme2MWuY2Ca2XDUcBoRkfyWnOe0d52GiBean9pSD8BLF4eZW9DrrYisjopOIpIyC9EYoQl3eV1LVbHhNJJv7KZyfB6LaDyOE9ISOxGRdOkbm13cCETznArPo5sbsCyYXYjy8qVh03FEJMep6CQiKdM/PkcsMUW8VZ1OkmJFPi93JZfY9aroJCKSLq90uoWGYMDL1uZyw2kk02qCAe5ZUwXAs+cGDKcRkVynopOIpEzyqmhliZ/yYr/hNJKPdra6S+zOhybV8i8ikiYvd7hDxPetr8Hn1ceFQvTmrY0APOMMEI/H73BvEZFb06uIiKRMz6hbdFKXk6TL1qYKfB6LSCzOGe1iJyKSFi8nOp0e2FBrOImY8qbEMPHukVkuDkwZTiMiuUxFJxFJmd6xGQDaqlV0kvQo8nvZ0uQu9TjRM2Y4jYhI/ukbm6Vr2H09V9GpcG1tLqe50p3P+YyW2InIKqjoJCIpMb8QZWBiHoBWFZ0kjXa1uXMmLg5MMTm3YDiNiEh+SQ6OLivysSOxa6gUHsuyeCzR7aSik4ishopOIpISXSMzJFf8t1WVGs0i+c1uKqfI5yEWh0OXR03HERHJK8mi0772as1zKnBv3uIWnQ53jTI+q4s8IrIyeiURkZS4NDQNuDuelAS8htNIPvN7PWxrdq++J3dYEhGR1Hj5kjtE/E5L64LBIMFgMBORxJA3bKol4PMQjcV5/vyg6TgikqN8pgOISH64nCg6aYi4ZMKutipe7R7D6Z+kf3yOpsTcCRERWbnesVmujLjznAJeD/udWxcaxhJzHDumr92nsaIovQElo0oDPh7cUMv+84M8c26Ad9/dYjqSiOQgFZ1EJCUuDbk7m2iIuGTCpoYySvxeZheifPdEH7/6yAbTkUREct7LHW73aJHPg8/roXds9pb3DYXczRwauVb0rw0G0htQMu6ntzaw//wgT58NEY7ECPi0UEZElke/NURk1cZnFwhpiLhkkNdjsaO1EoDvnLhqOI2ISH5IznNqrw3i9ViG00g2eNuOJjwWTM5FeOGCltiJyPKp6CQiq3aqdxwAC2itVNFJMuPuNrfodLx7jK7hacNpRERy38uJOXkb6jWrSVwN5cXcv96d7/U9XeQRkRVQ0UlEVu1Ej1t0qisvosivIeKSGe11QapK/AB861if4TQiIrmtZ3SG7hF3Od36OhWd5Jp37moG4KkzIeYWoobTiEiuUdFJRFbtZK8716FNQ8QlgzyWtbi70jde7SUejxtOJCKSu15J7FpX4vfSrK5luc47kkvs5iPaxU5Elk1FJxFZtePdbqeT5jlJpj20qQ6AzqFpjnWPGU4jIpK7DiTmOW1uLNM8J3mN2rIi3rDRfb393kktsROR5VHRSURWZXhqfnF3m7bqUsNppNCsrSllS1M54HY7iYjI8sXjcQ4kdq7b0lRhOI1ko+QSu6e1xE5ElklFJxFZlROJIeJey6K5svgO9xZJvffe0wrAd473EY7EDKcREck9XcMzixeQtreo6CSv97btTXg9FtPhKM85WmInIkunopOIrMrJnmtL6/xe/UqRzPvZ3a1YFozOLLBfsyZERJbtxYtDANQEA6ypUdeyvF5NMMAbNrpzFL97Qpt3iMjSpfQTom3blm3bP7Bt+7fvcL8/tm27z7btSdu2/8W27apU5hCRzDmemKOjnW7ElKbKYh5KzJr4xqs9htOIiOSelxJFpzdsrMVjaZ6T3Ny7d7UA8OOzA8yGtcRORJYmZUUn27Y9wN8Ab7/D/X4L+ATwy8CbgG3A36cqh4hkTjwe53iPW3TaoKKTGJRcYvf02QHGZxcMpxERyR3RWJyfJOY5PZzYnEHkZt66vRGfx2J2IcrTZ0Om44hIjkhJ0cm27Q3AfuBdwJ22D/p94P92HOeHjuMcBj4KvM+27bWpyCIimdMzOsvQVBiADfVlhtNIIXv7jiZK/F7CkRjf1846IiJLdqp3fLFY/5CKTnIbVaUBHrPrAfjq4W7DaUQkV6Sq0+kB4CSwBxi/1Z1s224CNgDPJ29zHOckMAo8lKIsIpIhyS3qSwNe2qpKDKeRQhYs8vG27Y0AfOOodrETEVmq5Dyn9tpSzXOSO3p8n9sn8MKFIbpHZgynEZFc4EvFQRzHeQJ4AsC27dvdtTXx7xs/EVwF2lby3NFoFMdxVvLQvDQz4/7y1zlZGp2v5bnxfD17wh3avLmumImJMUKhOzU63t5UlZepqTCh0OqGQafiOKk4RjgcJhqNMjU7ZTxLqo6TzizhsNs1Fwr1L/k4Qaro6ZlnenqafQ1xvgkcvDzCs4dO0VLhX1XGbFcov7+iUc0NEUmn5DwndTnJUrzJrqehvIiByXm+erib33/rbT/7iYhkfPe65OWT+RtunweKMpxFRFbp3KD7o7ytSVdGxbx7mkupK/UC8NTFCcNpRESy32w4yuHLo4DmOcnS+LwePnCv2yvw5OEeItGY4UQiku1S0um0DLOJfxcBk9fdXgSsqD/T6/XeqbuqoCSveOucLI3O1/Jcf74WojE6Ri4B8MjWNZQGfDRSvKrjlwXLmCdMY6PX+HFScYxQqB+v10v4h52ZAAAgAElEQVRZWYnxLKk6TjqzJDucGhublnycqqoS2trqF79+/H7422c7eO7yLH/x+Ga8nvzdhalQfn8dO3ZM3U4iaXLo8gjhaAzLgjdsVNFJlubxe9fyt8920D8xx/7zg7x5a6PpSCKSxTLd6ZTcy7r5htubef2SOxHJYk7/JPMR9+rW7rVVhtOIuD6wdw0AfeNzi0tGRETk5pK/J3e1VlJZmt9LkiV11taWLnbG/eshDRQXkdvLaNHJcZx+4BLwSPI227Z3AlXAgUxmEZHVeTUxRLyxoojmSg0Rl+zQXhfkvvU1ADx5pOcO9xYRKWwvap6TrNDj+9yLPM+cG2BgYs5wGhHJZmkvOtm2XWnbdv11N30a+HPbtt9p2/Ze4AvAk47jXEl3FhFJnWNX3KLT7jXqcpLs8oG97qyJH57uZ2wmbDiNiEh2GpkOc7rPnX+neU6yXG/d3kh1qZ9oLK6LPCJyW5nodPpr4NB1X38a+Dvgn4AfA2eAX8tADhFJoWPd7uDRu1V0kizzMzubCQa8hCMxvn28z3QcEZGslFxaV+z3sGddteE0kmuKfF5+fo97kecrh7qJxuKGE4lItkr5IHHHcdpv+PpjN3wdBz6Z+EdEctDE3AIdg9OAOp0k+wSLfLxrVwtfOdzNk4d7+MiD7aYjiYhknRcuDAKwr72GYv/qNoeQwvSh+9bwDy92cmVkhqfOhHj7jqVvBCIihSPTg8RFJA+c6B4HwLJgV5uKTpJ9kts5n+wd50xi+YiIiLji8Tj7z7tFp0c319/h3iI3t6mhnDclpqh89vkOw2lEJFup6CQiy5ZcWre5oZyyopQ3TIqs2t511WyoCwLw5BHtrCMicr3zoSlCE/OAik6yOr/+xo0AHL0yxuHLI4bTiEg2UtFJRJbtWLeGiEt2syyL9ye6nb75ai/hSMxwIpHsYNt2m23bT9q2PWTbdr9t2/9g27Z+mReY/ecHAGipLGZTQ5nhNJLLHthQw662SgA++/wlw2lEJBup6CQiyxKPxzmWWF6nIeKSzd63pw2PBaMzC/z4bMh0HBHjbNv2At8CyoA3Ae8BduPuJCwFZHFpnV2PZVmG00gusyyLX3/jBgCeOhvi0uCU4UQikm1UdBKRZRmYjjA05bbkq9NJslljRTGP2Q0A2s5ZxHUPsAf4ZcdxTjqOcxD4BPAedTsVjun5CIc63WXyWlonqfD27U2sqSkhHofPvdBpOo6IZBkVnURkWc4OzAFQ4veyuVEt+ZLdPrDXXWL3nDNAaGLOcBoR4zqBdziO03/dbcl9zosN5BEDXr40TDgaw+uxeMOmOtNxJA/4vB5+9WG32+nfjvYwODlvOJGIZBNNABaRZUkWnXavqcLnVd1astubtzZSEwwwMh3m34728FuPbTIdScQYx3GGgX+/4ebfAy7eUIhakmg0iuM4KcmWDWZmZgBy+u9UXFyM1+u97X2+f9Tt/NzeWMrEUIjr9/csLi5mYmqCUGj4js8VDocBCIWufetMVXmZmgoTCg0uP3weP/5m5yrVz7/a7EGq6OmZZ3p6ekWP310Ro7zIw+R8jL/6zhF+eW/tio4D+fGzmEk6X0unc2WGik4isixnBt2i0551Wokh2S/g8/Bzu1v5/EudfO1wD7/56EbNLxFJsG37PwHvA95tOoukhtfr5fRgmLlw5Jb32X/JLTOtqS7mSO/Ma/5sTZ0PjxZCyAoU+z28Z2slXzo2yrfOjPHe7VVUFd++ACoihUFFJxFZsrlIjI5ht2V677pqw2lEluYD97bx+Zc6uTQ0zZGuUe5trzEdScQ427Y/Cfw58LuO43xvJcfwer3Ytp3aYAYlr3zn+t+pY3qQ4fDsTf9seGqeoekFANY1VDF9w6rKQFEJZWVeGhvvXCxIdu00NjYt3lYWLGOe8JIefzP5+vibnatUP/9qs1dVldDWtroZX3+0doHvnHuGibkIT/fAH79zZT9L+fKzmCk6X0unc7U8R44cSclxdClDRJbswtA80cT0j3vWqOgkuWFrcwU7W93tnJ88rIHiIrZt/0/gvwC/5TjOp03nkcw5P+DuLFYa8NJSVWI4jeSbyhI/H390IwD/fKBLsxRFBFDRSUSW4UxintPG+iDVwYDhNCJL9wv3ugPFv3uij+n5Wy87Ecl3tm3/OfA7wEcdx/k703kksy6EJgHY3FiOR0uN5ToVxalZAPPLD7VTVxZgPhLjM89cTMkxRSS3aXmdiCzZmQG3XV9L6yTXvOfuVv7ie2eZDkf53omr/MK+NaYjiWScbdv3AH8MfAp4yrbt69f6DDmOo4psHotEY1wadIdE39Wg3WfltfxeDwc6hglHYit6fGNFEaMzC4QjMd62rYkvHbzCEwevcHdbFfXlRXd8fMDn4cGNKx8+LiLZS0UnEVmSeDy+uHPdnrUqOkluqSz1844dTXzrWB9fOdytopMUqvfhdrn/UeKf6+0ETmU8kWTM5eEZwlG3oLBJRSe5iXAkRu/YzeeB3UltMLD4+M1N5VSW+BmfXeCJg1d4/962Oz6+Vcs9RfKWik4isiSXh2cYn3ffrKrTSXLR4/vW8K1jfRzpGuV8aJLNjeWmI4lklOM4fwL8iekcYsa5fnfXurbqEsqL/YbTSD7zez28yW7gm8d6efXKKG+8q46GiuI7P1BE8pJmOonIkhzpGgWgLOBhY72ukErueWB9LetqSwH4yqFuw2lERDLL6XfnOdkquEsG7F1XTW0wQBz4wal+03FExCAVnURkSZJFp631xXg8Gj4qucfjsXg8sazu60d7mI9EDScSEcmMocl5hqfDAGxpqjCcRgqB12Px9h3u2DgnNLk4xF5ECo+KTiKyJK9ecYtOd7fpzapkj+XutvP+PW14PRajMwv86HQoTalERLLLucQH/vIiH81VWuYkmbGtuYL1dUEAvnfyKtFY3HAiETFBM51E5I67lcyEI4tt+QGfl/3O4Gv+vLHizruSiKTDSnbbubutkqNXxvhf+zuoKPZrxxwRyXvJeU6bm8rxWOpWlsywLIt37mzmb5+9yMDkPIe7Rrh/vV5vRQqNik4icsfdSi6EJokDFhCPzL3uvrXBQHoDitzGcnfb2dHqFp1O901wsnecna2VaUwnImLW3EKUy0PTgOY5Sea1VJWwZ201R66M8vSZEHe3VVHs95qOJSIZpOV1InJHXSMzANQFfQS8+rUhue2uhvLFZXmHu0YMpxERSa+LA1PE4uC1LO5q0EYgknlv2d5IwOthOhzlWWfAdBwRyTB9ehSRO7qSKDo1l6s5UnKf12Oxd101AEe7RjVjQkTyWnJ5/Pq6IEXqMBEDKor9vHFzPQA/uTjM4OS84UQikkkqOonIbUVjcboXi05+w2lEUuPedTVYwMRchJO9Y6bjiIikRSweXxwibjdpaZ2Y88hddVSX+onG43z3RB/xuC74iBQKFZ1E5Lb6x+eYTwxpbqlQ0UnyQ3UwwMbEMpPnbhiMLyKSL/rGZpmejwAqOolZfq+Hd+1qAeDCwBRnrk4YTiQimaKik4jcVuewO3y0qtRPRZHa8iV/3JtYYne8Z4zQxJzhNCIiqXcusbSurixAXZl2mhWztjSVLw6z/96Jq8vaeVZEcpeKTiJyW8kdb9bXBg0nEUmtbc0VlAa8xOLwtSM9puOIiKRccp6Tdq2TbGBZFu/a1YzXYzE2u8D+8xoqLlIIVHQSkVuKxeNcTnQ6tdep6CT5xef1sGet2+30lUPdxDRQXETyyMTcAr1jswDYTRWG04i4asuKeOSuOgCevzDE8JSGiovkOxWdROSWBifnmQlHAXU6SX5KLrG7MjLDy5eGDacREUmd84kupyKfh/a6UsNpRK55bHMDVSV+orE43z1xVUPFRfKcik4ickudiaV1wSIftWUBw2lEUq+hopi7EgPF//VQt+E0IiKp4yR2rdvUUIbPo7f8kj0CPg8/s7MZcL9Pk7PHRCQ/6RVIRG4pubRufW0plmUZTiOSHo9urgfg30/1MzodNpxGRGT1IrEYFwamAHd4s0i22d5SsXjR57sn+jRUXCSPqegkIjcVj8cXh4hrnpPks/vaaygv8hGOxvjGq72m44iIrNrloZnFD/GbNURcspBlWbx7Vwtey2J0ZoHvnewzHUlE0kRFJxG5qZHpMBNzEQDWq+gkeazI7+U9u1sAd6C4ZkuISK5z+icAaK0qobzYbziNyM3VlRfxcGKo+PdOXuXK8IzhRCKSDio6ichNJZfWFfs9NFYUG04jkl4f3LcWcGdLvNo9ZjiNiMjqJGfkaGmdZLs32Q1UlvhZiMb58++eNh1HRNJARScRuanOIfdqU3ttEI/mOUme29FawbZmd0vxrxzUQHERyV3947MMJ+bT2So6SZa7fqj402cHePpMyHAiEUk1FZ1E5KaSnU7ttVpaJ/mtotiHZVl86L41AHznRB9T8xHDqUREVuZYzzgAZUU+WqpKDKcRubMdLRVsb3Ev/PzZt08zt6Ch4iL5xGc6gIhkn/HZBUYSV0k1z0nynd/r4UDHMLXBIvxei5lwlL/6ocNjdsOyjhPweXhwY22aUoqILM3xxBJhu7FcncqSEyzL4iMPrOOT3zpN79gsTxwf4VfurTMdS0RSREUnEXmd5K51fq+lq6RSEMKRGGOzC+xoqeTV7jGeOhvirmXu+NSqnxURMWxqPoITcuc5aWmd5JKmyhJ+47GNfPrHF/jaqTHevKkC23QoEUkJLa8Tkde5lCg6rasJ4vXoKqkUjnvbawDoGZ3l6vis4TQiIsvz4oVBorE4XsviroYy03FEluW3HtvIutpSonH4zIEB7SYrkidUdBKR1+kYnAJgY72W1klhaa8tpa4sAMDhy6OG04iILM+PEkOY19cFKfJ7DacRWZ5iv5c//9kdAJzon+PrR3sNJxKRVFDRSUReY2Q6vDjPaaOukkqBsSyLe9e53U6vdo+yENUwUxHJDZFojGfODQCwtVlL6yQ3Pbq5nkfa3feff/n9s4zNhA0nEpHVUtFJRF4j2eVU7PdonpMUpD3rqvFYMLcQ43TfhOk4IiJLcrhrlLGZBQC2NlcYTiOycr9xXx0lPovh6TD/7w8d03FEZJVUdBKR17i2tK5Mu95IQSor8i1+YDt0ecRwGhGRpfnRaXdp3bqaUqpKA4bTiKxcXdDHR/a4u8F++eAVjl7RcneRXKaik4gsisXjdAxcKzqJFKp9iYHinUPTDE3NG04jInJ78Xicp872A3DP2mrDaURW72e3VrKtuYJ4HP74G6eIaLm7SM5S0UlEFoUm5pgORwHYpHlOUsA2NZRRVeIH4EiXrrCKSHZzQpN0j7g7bu5ZW2U4jcjqeT0W//W9O7AsOHt1gi8c6DIdSURWyGc6gIhkj4uJLqeqEj+1QbXmS+HyWBZ711Xz43MDHOka5ae3NuL1aLmpiGSnpxJL61qrSlhbU0rf+JzhRCKrd8/aaj5031qeeOUK/+NHDu/c2UxTZXHanu9AxzDhyMo6qgI+Dw9urE1xIpH8oKKTiCy6fp6TpXlOUuD2rqvmmXMDTM1HcPon2NZSaTqSiMhNPXXWLTq9ZVujXr8lr/ynt23hh6f6GZ4O8yffPMnnPnJv2r7Hw5EYvWOzK3psqzbfEbklLa8TEQAisRidQ9MAbNTSOhGqSgPc1ej+LBy6rCV2IpKd+sfnONEzDsBbtzUaTiOSWpWlfv703dsAePrsAN8+3mc4kYgsl4pOIgJA98gsC9E4ABvrg4bTiGSHe9e5A8XPhyYZmwkbTiMi8nrJLqeKYh/71tcYTiOSeu+5u4W3JAqqf/bt0wxOaoMPkVyiopOIANeW1jVVFFNe7DecRiQ7bGkuJ1jkIw7asllEstJTZ9yi009tacDv1Vt7yT+WZfFff24HFcU+xmYW+NNvnTIdSUSWQa9MIgJcGyKuLieRa3weD3sTO0Ed7holFo8bTiQics3k3AIHOoYAeMu2JsNpRNKnoaKYP333dgB+cKqf7524ajiRiCyVik4iwmw4Ss/oDKB5TiI3Si6xG5tZoCNRnBURyQb7zw+yEI0T8Hp41K43HUckrd63p5XHEt/nn/zWKQYmtEujSC5Q0UlEON03TiwOXstifa06nUSuV1deRHvi5+JQl5bYiUj2SC6te3BjLWVF2pRa8ptlWfy3n99JRbGPkekwv/fVY8Ri6kAWyXYqOokIx3rGAFhfH6TI7zWcRiT77GuvBuBs3wRT8xHDaUREYCEa49lzAwCLQ5ZF8l1zZQn//X27AHjp4jB/t7/DcCIRuRMVnUQKXCwW53i3W3Ta0lRuOI1IdtrRWkmx30M0HudVDRQXkSxwsHOEiTm3CK6ikxSSd+xs5sMPrAXgfzx1niNdI4YTicjtqOgkUuBO9I4vvmm1G1V0ErkZv9fD7jWJgeKXR4lroLiIGJZcWnd3WyWNFcWG04hk1p+8cxtbmsqJxuJ84svHGJ9ZMB1JRG5BRSeRAvfMWfdNa315EbVlRYbTiGSvfe3uQPHBqXm6hmcMpxGRQhaPxxeLTupykkJU7PfymV+8hxK/l96xWX73K68SicZMxxKRm1DRSaTAPX3WnQehpXUit9dcWUJrVQkAh9XKLyIGnbk6Qe/YLABv2dZkOI2IGZsayvmLn9sBwHPOIH/27dPqRBbJQio6iRSwq+OznLk6AcCWpgrDaUSy372JgeIne8eZW4gaTiMihepHp90up3W1pWxuLDOcRsSc9+9t4zcf2wjAl165wudeuGQ4kYjcSEUnkQL2TGLXm9KAl7U1pYbTiGS/u9uq8HstFqJxjiUG8IuIZNri0rqtjViWZTiNiFl/+Fabd+1qBuAvv3+O75+8ajiRiFzPl4qD2LbtA/4K+MXEMb8I/IHjOOGb3LcF6L3JYeodxxlKRR4RWZpnEkvrdrVV4vXoTavInRT7vexqreLIlVEOd43wwIZa05FEpMD0jM4sdilrnpPki4riax9Lg8Hgsh7r8Vh86gN30z8+x+GuUX7vK8cIFvl4dHN9qmOKyAqkpOgE/CXwDuA9QAD4AhAG/uAm990GjAFbb7h9OEVZRGQJZsNRXrzo1nl3t1UZTiOSO+5tr+bIlVH6xuboHZtdnPMkIpIKBzqGCUduPRD5qTP9AJQV+Ziej7LfGVz8s8YKbQgiucnv9Sx+74+NuZt1dEwP3uFR1wR8Hj73kXv5+b/7CZ1D0/zqFw7xPx+/h3cmOqBExJxVF51s2y4Gfgv4kOM4BxK3/Q7wZdu2P+k4zuwND9kOnHMcp3+1zy0iK3fg0hDzkRgeC3a2VTE+q61mRZZibU0pDeVFDEzOc/jyCK27W01HEpE8Eo7EFoeE38xPOtzrtJsby+ifmHvNn9UGA2nNJpJOye/9UMhdvt5I8ZIf21pVQnUwwJd/7QE+8vlXOB+a4ne+fJTJuZ188L616YosIkuQiplOu4Eg8Px1t+1P3Lb7JvffDpxLwfOKyCokd627d10NZUWpanoUyX+WZXFvew0Ax7rHbtuRICKSSlPzETqHpgHY3lJpOI1I9mmqLOarH3+Q3WuqiMXhP3/9JJ955gKxmHa1EzElFZ80W4Fpx3HGkzc4jjNh2/YM0HaT+28Hpm3bfgVYCxwGfs9xnIsrefJoNIrjOCt5aF6amXHbUXVOlqZQz1c0FuffT7ij1fa2FDE2Nrp4Vel2wmF3TFso9NpGxakqL1NTYUKhpbdB30w2HScVxwiHw0SjUaZmp4xnSdVx0pnlVt9f6c6zkmO0FcfwWjAfifGTs1fY2lBMkCp6euaZnp5ecZblKJTfX9GodgkUSTrbN0EcdynRpgbtWidyM1WlAb70q/fz8S8e4cWLQ3zqR+c5dHmUT33gburLtQRVJNNS0elUCszf5PZ54GY/1VuBcuD3gfcCcWC/bdsaKiOSISdDs4zMuh/k3rRJV0pFlqvE72FjjfsSdyp062UwIiKpdKrPvca7pakcv1ebUIvcSrDIxz987F4+uG8NAPvPD/KOv36B58+v7qKZiCxfKjqdZrl5cakImLnJ7W1A1HGceQDbth8HrgDvB/5+uU/u9XqxbXu5D8tbySveOidLU6jn659OnwRg95oq7tu2kf3O4JLWzSc7UBobm15ze1mwjHnCNDZ6V5Urm46TimOEQv14vV7KykqMZ0nVcdKZ5VbfX+nOs9JjPGyVcf6lTvomI1ilVVRVVdPWlrmdcgrl99exY8fU7SSCuwFIx+AUADu0tE7kjop8Xv6f9+3i4bvq+D+/fpKhqXk+8vmDfOi+tfzh22xqNANNJCNScYmkBwjatl2evMG27QrcDqjeG+/sOM5MsuCU+HoW6OTmS/FEJMUWojF+cOoqAO++u8VwGpHctaE+SHWpH4Ajl0cNpxFZHtu2Ldu2f2Db9m+bziJLc/bqBLE4+L0WmxvL7/wAEQHgXbta+P4nHuGete7Cmi8fvMKbPvUcXzxwmahmPYmkXSqKTseBaeCR6257NHHb8evvaNt2q23b47ZtP3LdbeXAZuBsCrKIyB28eHGIsZkFLAveuVPbyIqslOe6geJHr4wSiWqguOQG27Y9wN8AbzedRZYuubTObiwn4NPSOpHlWFNTypMff5A/e/c2yot9jM8u8MlvneZdf/OiltyJpNmqX7ESnUqfAz5j2/Yjtm2/EfeNzN86jjNn23albdv1ifv2AkeBT9u2fb9t27uALwP9wNdXm0VE7uw7x/sAuK+9hqbKpW9FKyKvt2dtNRYwHY7yavedh/GLmGbb9gbcXYbfBeibNkfMLUS5MOAurdveqqV1Iivh83r45YfW8+wfPMbj97qzns5eneAjnz/Ih//+FbqGM7MRiEihSdVlkv8M/Aj4Dm7x6FvAHyf+7K+BQ9fd9xeAE4n7HgCiwNscx1lIURYRuYW5hSg/Oh0CtLROJBUqS/zYTe4yl/26Uiq54QHgJLAHGL/DfSVLnOufJBqL4/NYbNHSOpFVqSsr4r+/fxff+t8e4oENbsfyixeH+NNvn+bJw91MzOljqUgqpWKQOIkZTb+R+OfGP/vYDV8PAh9NxfOKyPI85wwyNR/B67F4x46lD2sWkVvb117Duf5JTvWO0z0yw5qaUtORRG7JcZwngCdg9UPoo9Ho4kD7fDAz4+5/Y/LvFAwGGRubIRR6bRPakUtufXBtpZ+xkVsXuKeqvExNhQmFVlYEX87jw+EwcG0TiEw/fy49/mbnKtXPn61/95U8fqnn63pBqujpmWd6eundSsXA//XGag5tKOIfj45yaWSOV7vHON03zn1tpexuLsHrsdL2/KmSDb+7coXOlRlaEC5SQL5zwl1a99CmOmrLbrbppIgs1+bGcsqLfcSBJ4/0mI4jInkmHI1zecz9EL6pVq/dIqlkWRb3rQnyjx/czOO7GyjxWYSjcV7smuZLx0e5OqmuJ5HVSkmnk4hkv+n5CD8+m1hat0sDxEVSxeux2LO2mv3nB3nycDe/++a7lnxlVCSXeb3eVXdLZZPklW/Tf6eO6UEauTZz8UTPGNEYeC2L++01lAS8t3xsWbCMecI0Nt76PreznMcnu1AaG691Tmfy+XPp8Tc7V6l+/mz9u6/k8Us9X9erqiqhra1+Rc+d9DOzxWxureOpsyFeuTTM6GyUr50a46e2NPCY3YDHuvVreyqef6Wy5XdXLtC5Wp4jR46k5DjqdBIpEE+dCTG3ECPg9fDW7VpaJ5JK966rBuDq+Jx2wRGRlDre4y6tu6ux7LYFJxFZvZKAl/fc3cJv/9QmmiqKicXh6bMDfO6FS4xOh03HE8lJKjqJFIgnXrkCwJu3NlBZ4jecRiS/1JYVsa25AoB/PXTFcBoRyRez4SjnQ5MA7GqrMpxGpHA0V5bwm49t5A0bawHoGp7hb569QOeQdrgTWS4VnUQKwPnQJAcvjwDwS/evM5xGJD+9cbPbVv/jswMMTM4ZTiMi+eDM1XGisTh+r8XWZu1aJ5JJfq+Hd+1q4aMPthMMeJlbiPH5lzo50TN25weLyCIVnUQKwJde7gKgvbZ08YqNiKTW3rXVVJX6icTi/NuRXtNxRCQPJJfWbWmqoMinpXUiJthN5fzmY5uoKysiGovzr4e6eeHCIPF43HQ0kZygopNInpuej/D1o+4H4F+6fx0eDTgWSYuAz8N772kF4ImDXURjejMq2c1xnHbHcT5jOofc3OTcAh0DUwDcraV1IkbVBAP8xqMbWFdbCsAPTvXz76f6VXgSWQIVnUTy3LeP9zE5HyHg8/D+vW2m44jktQ8/4C5f7R6ZZf/5AcNpRCSXneodJw4U+z1sbiwzHUek4JUGfPzKQ+vZ2VoJwAsXh3j6rF7rRe5ERSeRPBaPx/mXxNK6d+1qpjoYMJxIJL9trC/j4U11APzzgS7DaUQklyWX1m1vqcTn1Vt2kWzg93p4fN8a7m5zC0/POgPsd1R4ErkdvYKJ5LHjPeOc7psANEBcJFM+8qD7s/acM8hl7XIjIiswOhPmysgMoKV1ItnGY1m8f++axV1rf3gmxI/O9BtOJZK9VHQSyWPJLqetzRXsWas3rSKZ8OatjbRWlQDXfgZFRJbjZKLLKVjkY31d0HAaEbmR12PxwX1rFpe+fumVK3zzVW0iInIzKjqJ5KmhqXm+c7wPgA8/sBbL0gBxkUzweix+8f61AHz1cDez4ajhRCKSa44ntmTf2VqJVxuAiGQln9fDL92/brEw/EdfO8HBzhHDqUSyj4pOInnqs89fYj4So6rUz8/tbjUdR6SgfHDfGgJeDxNzEb59XFc+RWTpuoanuTo+B8DuxNwYEclOfq+HD9+/jubKYsLRGB//4mEtrRe5gYpOInloaGqefz5wGYBfe2QDwSKf0Twihaa2rIh37WoG4As/6dKWyiKyZC9eHAKgrqyINTWlhtOIyJ2UBLz8H2/ZTE0wwOjMAr/yT4cYmwmbjiWSNVR0EslDn3v+EnMLbpfTR9/QbuTM2fQAACAASURBVDqOSEH6D4mB4meuTnCka9RwGhHJBeFIjAMdwwDsXVetpfEiOaKhvJjPfWQvAZ+HS0PTfPyLR1iIxkzHEskKKjqJ5Bm3y8kdXvxrj2ygTF1OIkbsXlO1uKXy37/QaTiNiOSCZ86FmJyPYAH3rNEGICK5ZO+6Gj71gbsBeKVzhP/yndOGE4lkBxWdRPLM556/xOxClKpS/+LW7SKSeZZl8R8f2QDAD8/00zWsGQ8icntPHu4BYHNjORUlfsNpRGS53nN3C5/4qU0A/MvLV/jSK9rFVkRFJ5E8MnxDl1N5sd6wipj0MzuaaK0qIR6Hf3zpsuk4IpLFBibneO78IAB71lUbTiMiK/W///Rm3rKtEYA/+9Zp7WgnBU9FJ5E88r/2d6jLSSSL+LwePpaYq/bVw92MzyyYDSQiWesbR3uJxuIEi7xsbSo3HUdEVsjjsfj/Ht/N5sYyIrE4v/kvR+gdmzUdS8QYFZ1E8sT50ORiJ4W6nESyx+P3raGsyMdMOMoTB6+YjiMiWSgej/PkEXdp3Rs21OHz6i26SC4rK/LxuY/cS1Wpn+HpML/+z4eZDUdNxxIxQq9oInkgHo/zJ984RSQWZ31dkP/48HrTkUQkoaLYzwf3rQHgn37SSTii3WxE5LWOdY9xceD/b+/O46Oq7v+Pv2bLSgKELeybcFiURVCLguKOW63WWqW12n6rtWq1tVrt126/b1u7qLXi8q21/da9tGq17juCVUBBNlkOS9gDIRCyL5PJzO+PO8GIQXKTydxJ8n4+Hjgzd5l8cpy5+dzPPfecSgBmjOrtcTQikghDe2Vz/+yjCfh9rC4s5+anVxCLxbwOSyTpVHQS6QSeXrqDD7Y494v/8vwjyQgFPI5IRJq64oRhBPw+isrreHFlodfhiEiKmfvBdgDG5OcwJC/L42hEOp7cjNScrfmEI3pz29ljAXhx5S4eeGeTxxGJJF9qfjtFpMX2V4W5/eW1gDNjxnRdIRVJOYN6ZnHWkfm8uHIXf15QwAWTB+Lz+bwOS0RSwP6qMM8t3wnA174wVMcGkVYIBfws3LSv1b2J++WmJziiT3zzhGGs3VXOU0t3cOfrljH5OZw6tl+7/TyRVKOeTiId3O9eXcf+6npy0oP85NyxXocjIodw1YkjAFi3u4K31u7xOBoRSRX/WLKdukiUnPQgF04e6HU4Ih1WOBJlZ2lNq/5FGtrvtjefz8evLjiSo4f0IBaDG+Yux+6uaLefJ5JqVHQS6cDe37iXuR86XfJvnmXom5PhcUQicigTBvXgpNF9AJjz9gaN6yAiNERjPLZwKwAXTR1EdrpuQhDpjNKDAf502RTyczOorIvwzb99QFF5rddhiSSFik4iHdSeilqun7scgImDe/C144Z6HJGIHM71p44CYOWOMt5ZX+xxNCLitTfXFh2YSv0b04Z5G4yItKu+ORn89YqpZKcFKCyr5VsPf0hlXcTrsETanYpOIh1QQzTG9+cuZ29lHTnpQe69ZDIBv8aAEEl1U4b2ZPoRzrhrc95SbyeRru7RhVsAmGn6MLx3tqexiEj7Gz+gOw98fcqBGe2ufeIjIg2a1VY6NxWdRDqge9/ewPub9gHw+4smMKSXZroR6Sgaezst21bKexv3eRyNiHhlQ1HFgWPA5ccP8zYYEUmak0b34ddfOhKA+euLue3Zj3URSjo1FZ1EOpj3Nu7lnrc2AHDF8cM466j+HkckItDy6ZqPHZ7HF0bkAXDPW+uVaIp0UY8s3ALAsF5ZnDSqj6exiEhyXXLsEK49eSTgTCbwi+dXKx+QTkujFYp4pDXTuhaV1/LLF9cQi8GI3tn8+Owx7RSdiLjlZrrmmaP7sqighA+37OfB+QWM7Z8LQFrQz7SRvdo7VBHxWFlNPf/6aCcAl00bhl+3yIt0aC298NTUTWcYSqvreWLxNh5ZuJVgwM9PzhmLz6fjgXQuKjqJeKRxWteWqqyL8Kf5m6ioi5AZCvDdmSNJDwbaMUIRcaul3+ucjCBDe2WxdV81TyzeytUnjcTn8zGwR2YSohQRrz383haqww1kpwW4aMogr8MRkTZyc+GpqdPG9mN7STULNuzlr//ZTDDg49ZZY1R4kk5FRSeRDqAu0sAj72+hpCpM0O/j8mlD6ZuT4XVYItJKPp+PM8bl89C7BWzfX8OqnWVMGNTD67BEJAkqauv5638KAGcsp+6ZIY8jEpFEcHtBudEZ4/OJRGO8v2kfD84voK4+yk/PHadJgqTT0JhOIimuIRpj7gfb2Vlagw+45JghDOmlGW5EOrrhvbMZF7+t7vU1RZq9RqSLeHThVsprI2SlBfj2jBFehyMiHvP7fFw5fQRfnDgAgIff38I1TyylJtzgcWQiiaGik0gKa4jGeHrpdmxRBQBfnDSAcQNyPY5KRBJl1vh8/D4oqQqzaHOJ1+GISDurrIvw0LtOL6fLvjCUvOw0jyMSkVTg9/u4+6uT+NYJwwF4bXURlz60iL2VdS3aPyNDd0BI6tLtdSIpqiEa46ml21m5owyAk00fjhuuAYZFOpPeOekcO7wXiwr2MW/dHs7RbJQindrji7ZSWl1PRsivXk4i8ikBv4+fnTeOwXmZ/M+La1i+vZQLHniPey89mkmDP/8W/EAg0KoxpRppIhNpTyo6iaSghmiMfy7ZzqqdTsHppNF9OG1sP4+jEpH2cMqYvizbtp+a+gZeWFHIORNUeBLpjKrDER5a4PRy+tpxQ+mTk+5xRCKSir55wnAG9sjk+rnL2F5Sw5f/931+cNoovjvziM8d56m1Y0oBmshE2pVurxNJMZFo9FMFp5mj+3DGuH6axUKkk+qWHmTm6D4AvLG2iILiSo8jEpH28OTibeyrCpMW9POdE9XLSUQO7Yzx+Tx37QmM7teNhmiMO19fz6UPLWLH/mqvQxNxTUUnkRQSjkR5fNHWAwWnk00fTlfBSaTTO/6I3vTICtEQjfHjf60iGo15HZKIJFBpdZj7520EYPaxQ+ibq/FXROTzjcnP5fnrpnP5tKEAfLC5hNP+MJ8/vrleg4xLh6Kik0iKqK6L8Nf/FLC+yOnlcNrYvpw2VgUnka4gFPBz/sSBACzeXMI/l2z3OCIRSaS7Xl/P/up6uqUHuebkkV6HIyIdREYowP87/0j+evlU+uakU1sf5Y9vbuDUu97h+RWFukglHYKKTiIpoLQ6zIPvFrB9fw0+4PxJAzhljApOIl2Jyc9h2ghnEM9fv7yWPeW1HkckIomwprCcJxZvBeD7p42ib456OYmIO6eO7cfbN83kmpkjSQv4KSyr5fq/L2PWPQt4c2M5kQYVnyR1qegk4rHC0hr+NH8TxRV1BPw+Ljl2iGapE+miZh83hJ5ZISpqI/z8+dVehyMibRSLxfjF86uJxuCIvt24/PhhXockIh1Ut/QgP5o1hjdvPIlZ4/MBWF9UyR3v7uGSx9fy2urdVIcjHkcp8lkqOol4aN3ucv68oIDy2gjpQT9XHD+MowZ2b9G+uRmafFKks8nNCPHTc8cB8MrHu3lt9W6PIxKRtnh+RSEfbCkB4OfnjSMUUOotIm0zpFcWf7psCi9fP4PzJg7A74PdFfU8+cE2fvvKOuZ+uI1NxZVEY+r9JKlBZ60iHnlzbRGPL9pKDOiRGeIbxw8j38XAoqGAn4Wb9hGORNsUR79cTdkskkoumDyQZ5ft5N0Ne7nt2VVMHtxDgw6LdEBVdRFuf3ktAGeO78eMUX08jkhEOpNxA3K599LJXHhEiH/bSl61+6mtj7JyRxkrd5SRl53G1KE9OXpoT3IzQl6HK12Yik4iSRaORPnli2t4bJEzvsPAHplcNm1oq/4YhCNRdpbWtCmeXtlpbdpfRBLL5/Nx+wVHcc6cd9lbGeZ7f1/GE98+jqB6SIh0KLe/vJai8jrSg35+cs44r8MRkU5qQG6Im2YO4uxJw3htzW4+3LKfbSXVlFSFeX1NEW+uLcLk53LssDxG9euGX2PGSpKp6CSSRHsr67jm8Y8OdLUf1z+Xi6cOJi2ok0kR+cTgvCzu/MpErnpsKYs3l3D3m+u5+cwxXoclIi30yqpdPLF4GwDfP200g/OyPI5IRDq79FCAKUPzmDI0j6LyWpZsKWHZ9lKqww2s3VXO2l3l9MgKccywPKao95MkkYpOIkny8c4yrnp0CYVlzoxU508cwDHD83S1QUSadcb4fK6cMZyH3t3M/fM2MXVYHiebvl6HJSKHsbO0hlueWQnA9CN6850TR3gckYikuraM1ZqdnU1GRgZUfTKGU7/cDM6ZMIAzx+ezZlc5H2wpoaC4itLqet5YU8Rba4sY2z+XY4fnMbJPt0T8CiKHpKKTSDuLxWI8sXgb//PiGsKRKFlpAf5w8UQyQ8E23xonIp3bj2aNYenW/Xy0rZQf/GM5L35vOoN6qseESKqKNES54e/LKK+N0Cs7jT9cPBG/XxeXROTztWWs1tLSagb3DgKBz6wLBvxMGNSDCYN6sLeijg+2lPDRtv1UhxtYXVjO6sJy8rLTOPvI/hw3Io+M0GffQ6StVHQSaUfltfX8+JlVvLRqFwBDe2Xx58umYvJzmG+LPY5ORFJdKODnvtlHc86cd9lfXc83/u8DnvrONHp10wQAIqlozlsbWLJ1PwB3XjxRkwCISIu1dqzWoqJS+vXIprmiU1O9c9I5+6j+nD6uH6sLy/hgcwlb9jljPz2+eCuvfLyLb00fzte/MJTumbr1ThJHA8mItJPl20s5d85/DhSczpnQnxe+Nx2Tn+NxZCLSkQzokckDX5tCWtBPQXEVV/ztQyrrIl6HJSIH+ddHO5jz9kYAvj19uG6HFZGUFAr4mTS4J1edOJIbTh3FMcPyCPp97KsKc8drlum/fZvfvrKOPRW1XocqnYSKTiIJVlvfwG9eXsuFD7zHtpJq0oN+br/gKO67dLIG7BORVpk2shf3XjoZvw9W7SzjykeWtKoLvoi0jzfWFHHz0844TscOy+PmWcbjiEREDq9fbgYXTB7oTF5y4giy0wJU1EX40/xNTP/dPG57dhXbS6q9DlM6OBWdRBJo6dYSzp7zLg8uKCAagzH5OTx37QnMPm4IPg0YLiJtcOb4fH775QkALCzYx+3zi1R4EkkB72/ay7VPfkRDNMb4Abn85YqppAc1LoqIdBw9s9L477PH8t6tp3Dj6aPpmRUiHInyxOJtzLzzHW5+agVb9lZ5HaZ0UCo6iSTA7rJabnpqBRf9aSEFxVUE/T5uOHUUz183nbH9c70OT0Q6iYunDua2s8cCsHBbFbe+XkhJVdjjqES6ro+27T/Q83BE72we+dax6tUsIh1Wj6w0rj91FO/dego/P28c/btn0BCN8dTSHZxy1zvc+I/lbCqu9DpM6WA0kLhIG1SHIzw4v4A/Lyigpr4BgHH9c7njKxMYP6C7x9GJSGd05YkjiBHj9pfXsbqolgsfeI+/ffNYhvfO9jo0kS7l2WU7uPWZVdRFogzonsFj3z6O3hrkX0Q6gay0IN88YTizjxvCM0t3cv+8jewsreFfy3by7PKdnDthAN875QhG99NYtXJ4KjqJtML+qjCPLdrKI+9vYV+8l0HvbmnceLrh4qmDCAbUiVBE2s9VJ47EX1PK7xcUsWVfNRc+8B73zz6a44/o7XVoIp1eQzTG715dx58XFADOzLR/u+IYBvbI9DgyEZHESg8GmH3cEL4ydRDPfrST++ZtZFtJNS+sKOSFFYXMGp/PVSeN4OghPb0OVVKYik4iLtjdFfzhjfXMs3sOjKUSCvg4c3w+5x41gMy0AO9t3HfY9+mXqyuhItI2M4Z1o3dWkF++s4d9VWFm/2Ux35g2lFtmjSE7XX/eRdrD7rJafvTMShasLwZgxqje3HvpZHpkpR3YZuGmfa0eb035gYikolDAz8XHDObCowfy7+WF3DdvI5v3VvHq6t28uno3U4f25NszRnD6uH4E/BrHVj5NWanIYRSW1vDCikKeW17I2l3lB5ZnhPwcO6wXx4/sRW5miJLqMLRwcode2WmH30hE5DDG9s3g2WtO4Htzl7FieymPLtzK2+v28PsvT1CvJ5EECkei/N97m5nz1gaqw87t9FfOGM4ts8Z8pndzOBJlZ2lNq36O8gMRSWXBgJ8vTxnElyYP5KVVu/jzgk18vLOcJVv3s2TrUgb2yOTiqYO5+JhB9O+u3p/iUNFJ5CBlNfUs2VLCuxv28p+Ne9m459OD5fXJSeeYoT2ZOiyPjJBmpxERbw3plcUzV0/jr//ZzF1vrGfH/hpm/2UxJ5s+3HDaaCYN7uF1iCIdVjQa4+11e/jNK2vZVOzM3NS7Wxo/P288500c4HF0IiLeCPh9fHHiAM6b0J9FBSU89G4Bb6/bw87SGu5+cz33vLWek01fzps4gFPH9iVHEyx0aQkpOhljgsBdwOz4ez4G3GStbXZKHWPMbcC1QA7wb+A6a21pImLp6jIyMggEVAhpifqGKPvCQbaV1jJv9yY+3lnGqp1lbN332e5KedlpnDuhP+dPGkhFTT2FZbUeRCwi0rxgwM93ThrJqWP7cfPTK1i2rZR5tph5tpiTRvfh6pNGctzwPPzq8t7luc3Zuqry2nqeWrKDxxZuYUs8L/D74BvThvGD00fTPVMnUCLSeeRmtK4s4PP5mDayF9NG9mLz3irmfriNp5fsYF9VmLfW7eGtdXtIC/o5aXQfzhjXj+mjenvaAyo7W5OueCFRPZ1uB84CvgikAY8AYeCmgzc0xlwDXA98A9gH/Bn4C3BRgmLp0gKBAKuLw2yqKm7T+6QF/Uwb2StBUSVfLBajoi7CrtJaCstq2FVaywebSyiuqKWkKsy+qjD7KsM0xGLN7u/3wYg+3ThyQC7jB3RnRJ9sgn4/2WkBKmsjSf5tRERa5oi+3Xjm6uN5fU0Rc97awJpd5cxfX8z89cUM6J7BeZMG8MWJAxjXPxefTwWoLqrFOVtXU1rbwIc7qrhnyVLescUHbqMDZ+ym/z57LGP753oYoYhI+wgF/G0aj67x3PHHZ43lh6cb3lhTxHPLdzJ/fTHhSJQ31hTxxpoiAEb0yeaEkb2ZPKQHRw7szoje2QmZhKkl8ZeWOhcRDj5X7ujnvqmuzUUnY0wGcA1wqbV2YXzZ94C/G2N+aq09+Kb2HwK/sta+Ft/2cmClMWaItXZbW+PpLKLRGJXhCGXV9ZTV1FNeE3+sdR7LauqpDjfQEI1R3xCjIRol0hCjvLKKvVUR6vET9PsIBfwEA35CAed5yO8jGPCTEQqQGfKTkRYgMxSIvw6QFvTj9/lSdgaWSEOUkqoweyrqKK6oY09Fbfyxjj3ldRRXfrKstr5lB82AD3rnpNO/eyYDe2QyoEcmA3pkkB78pMdYUXkdoLEWRCT1+f0+Zh2Zz5nj+/Hm2j088M5Glm0rpbCslgfnF/Dg/AJ6d0vnuOF5HDs8jylDezKyTzcy09RLtrNrRc7WaYUjUQr2VrJyRxmrdpSxckcpK3eU0fRSVFZagAuPHsjl04YxStOCi0gn15bx6JqeO6YF/ZwzoT/nTOhPRW09b63dwysf7+L9jfuoqItQUFxFQXEVjy3aCjjj5Jp+OQzplc2QvEyG5GXRNzeDvKw08rLT6J4VIjMUIOj3feaCWSwWIxqDSDRKRU092/fXUN8Qjf+LOY/x8+T6hih7S/YTicbIzK4kGv3kiN89M8TqwrIDr30+HxkhP1lpATJDQTLTAvHnATLTAuSkB+mWESQzFNBFvBZIRE+nSUA2sKDJsvnxZZOAhY0LjTH5wIim21prVxlj9gMnAB266FTfEKUuEqWuvsF5jESpizRQE26gqq6Byrp6KmojVNRGqKxz/lXURg4UlMqaFJbKa+qJNt8Jp135gIxQgG7pQfrmptM9M0RuRojczCC5GSHndeYnrzNDAUJBP6GAn7SAn7Sgj7RAgFDQh9/nIxo/EESjMaKxGA3RTw4M1WGnbarDDVSHI/HHBqrrIuyvrqe0Osz+6vCB5yVVYcpb0csoFPCR3z2DrPgBo3um83vkZadBbTk56X765/dPfGOKiHjI5/Nx+rh+nD6uHwXFlTy/opB/Ly9k894q9lbW8dKqXby0ald8WydhHNmnG4N6ZtIvN4O+Oen0yUknOz1IdlqQrPTAgcesUCAhVyUl6Vqcs3Uk9Q2f5BRV4ciB3KIq7ORYeyvD7Kt0Llbt2F/DtpJqCstqaK6zc0bQx4mj+3LKmL6cdVR/3UYnItIGORkhvjR5IF+aPJBIQ5SVO8t4b8NeFm3ex8c7yymrqae2PsqKHWWs2FH2ue/l90F6MIDfB5Goc14ZafUJc+XhN2kBvw+6pQfJyQiRkxGkW7wY5SxzlndL/2R5Y7GqcXlGyDmPdjqK+EiLP+9sMwD6Yoe4vailjDFfBh6x1nY7aHkVcIW19qkmy6YAS4A+1tq9TZavBh621t7h5mcvXbo0ilMnSbhww6E/xLHPPEkSn/PL+uBTFVXfQdsQ+2xosSb/aVzXxv/1KcHnc9rCf+A5+PAdeO6PPwdoiPGZX7rxVUs/RH6fD+dt2tZ4iXgfL2I5VHulUrsk6n0S8R6x+PuQArEk6n3aMxa338dExZNK7YvPR8DX9lg+TzQGDbEY0ajz2KYf5fvkwblo4W+fP8oQmzJliqpcbeQmZzuc9szBaiPOharmJDoH8/mc767f5/R89vs+yRta936+ZvONlmrrcSSZ+zd3zO5I8Sdz/5b+fWvLz0/V3701+3uRD3Tk/RORb7Y5h4nnL24d6KAQc57HmjwmnO/QnykfOH8QmslLY01fJNvnxNxUwOcjPdhuhao252CJ6OmUBdQ1s7wOSG9m28Z1h9u2JaKAHyg/3IZupQV8pLXmm5PyOuPv9Hk+OWj5IQG/fixe9EuF91Es7fs+iYuFFIolldoldd4ntWJp7wsC/viJdYIOismQi/P3XtrOTc52OO2Wg2UEG8uYHU8sFmvjV6utxxHt33X378ixa/9E7I/HP781+YsPp+DvnHb7miz1Ssf829OOEpKDJaLoVEPziUo6cPA0YDVN1lUcZtvDmjJlSqIGQhcRERHp7NzkbJ9LOZiIiIi0RCK6qu8Aso0xB0ZYNMbk4lxN29nMtgAHD6DTv5ltRURERCRx3ORsIiIiIm2WiKLTCqAKmNFk2UnxZSuabmit3Q0UNN3WGHMU0IMOOniliIiISAfR4pxNREREJBHaPJA4gDHmbuB84HKcGyEfBf5hrb3FGNMdSLPWFse3vQH4aXzb3cBDwAZr7VfbHIiIiIiIHNLn5WyeBiYiIiKdUqLux78VyAReACLAE8Bt8XX3ADOBYfHXc4DewMNACHgRuCZBcYiIiIjIoX1eziYiIiKSUAnp6SQiIiIiIiIiItJUIsZ0EhERERERERER+RQVnUREREREREREJOFUdBIRERERERERkYRT0UlERERERERERBJORScREREREREREUk4FZ1ERERERERERCThgl4HIO4ZY4LAXcBsnP+HjwE3WWvDh9j+NuBaIAf4N3CdtbY0SeF6zk17GWN6Ar8HzgXSgHnADdbancmL2FtuP19N9rsKeNBa62v/KFNDK76LPwC+D/QC3gWuttZuTVK4nnP5XUwDfh3fNgt4B7jeWrs9aQGnCGOMD3gZeMlae9/nbNelj/Uih6P8yZ025AMPAhXW2pvaP8rUoXzTHZftlQ/MAc4AGoCn4ttWJi9i7yg3d8flZ2sA0Nz3ro+1dm+7BpoiknE+o55OHdPtwFnAF4EvxR9vb25DY8w1wPXAN4GTgXHAX5ITZspocXsBfwMmAecDM3ESzeeNMYH2DzNluGkvAIwxg4E72j+0lOPmu3g18EvgRuBYnIP6U8kJM2W4+Wz9BLgQuASYDmQDzyUhxpRijPED9wKzDrOdjvUih6f8yZ3W5AM/Aq5q/9BSkvJNd9y019M4J7gzcP4eTgPuT0KMqUK5uTtu2mscUAr0P+jfvvYPM2W0+/mMLxaLJSpYSQJjTAawF7jUWvtCfNl5wN9xKrI1B22/Cfijtfbe+OujgJXAUGvttqQG7wE37WWM6Q0UA1+w1i6OLxsI7AAmWWtXJDv+ZHP7+Wqy32tABnBiV7ma4vKz5QO2A3daa/8YX3YE8DYwrStc2WzFsWs58A9r7W/ir48H3gN6W2u7RCJgjBkBPAIMBroDPz1UT6eufqwXORzlT+60or164xTlZgBVwD+7Uk8n5ZvuuGyvHOBB4GfW2o3xZdfj9MQYkvTgk0y5uTutOHbdAFxirZ2W9GBTQLLOZ9TTqeOZhHPFf0GTZfPjyyY13TDeFXVE022ttauA/cAJ7R5pamhxewE1wNnA8ibLovHHzPYKMMW4aS8AjDHfBvKAP7R7dKnFTVuNAwbS5EqAtXajtXZIVyg4xbn9bJUAFxhj8o0x6cA3gI04x6+u4gvAKuBooOxQG+lYL9Iiyp/ccXvMHgfUxtcVtHt0qUf5pjstbi9rbYW1dnaTgtMo4HKcE92uQLm5O27bazywLglxpaqknM+o6NTxDASqrLUHTkCsteVANTComW3hs/ep7mpm286qxe1lra2y1r5ira1rsvhGoBzn6mZX4Obz1dh19zfAt3Duse9K3LTVEUAdMMIYs9AYs8sY80z8PvKuwtVnC/gh0BvneFUFfBk4z1obbWbbTsla+6S19hprbclhNtWxXuTwlD+54+qYba1dYK29pCuOuxenfNMdtzkBAMaYl4D1OAWV/27vIFOEcnN33H62xgMDjTGL4/n5C/HeO11FUs5nVHTqeLJw/mcfrA5Ib2bbxnWH27azctNen2KMuQQnCbjVWlvdDrGlIrft9RBwX/wKcFfjpq1ycI63D+HcI/0lnNulXo0P3tcVuP1sjQD24AyyOh1YBjxljMlqZtuuTsd6kcNT/uROq/OnLkr5pjutba9bcXKCncDrxphQO8SWTeoS8QAACUJJREFUapSbu+O2vcbi5Ok/BC4AYsB8Y0yPdoswtSTlfKarnOx0JjU0/4VJx6lIHrxt47qKw2zbWblprwOMMVfgjE3wR2vt/7ZPaCmpxe1ljPkvnIH2Pncgw07MzWerHggBtzS5X/pSYDfOAKJvtl+YKcPNZysXZyyjC621r8eXXQRsAy4GHm7XSDseHetFDk/5kzutyp+6MOWb7rSqvRoLKfGcYCfObHYvtUeAKUS5uTtuP1uDgIbGnofGmK/i5JsX0TUmj0jK+Yx6OnU8O4Ds+KB6wIETtCw+2w18R/yx/0HL+zezbWflpr0a1/8AZ1aRO6y1P0xKlKnDTXt9HRgD7DfGVBK/v9cYU2mM+VqS4vWSm7YqjD9+3LjAWluMM3Df0HaOM1W4aa8xOPeSHxhMNd7Vdz1ODyj5NB3rRQ5P+ZM7rvOnLk75pjstbi9jTJ4x5tL4bK4AWGt348wu1idJ8XpJubk7rr6L1trqpre6xgfO3kzXuZU6KeczKjp1PCtwxjeZ0WTZSfFln5rtIn5ALmi6bXz2lR7AwnaPNDW0uL0AjDHfwhl07yfW2h8nJcLU4qa9vo5zH/Sk+L/r48snAc+3b5gpwU1bLcO5kjClcYExpi/OmEVdZcBVN+3V+EdtYuOC+OwaI4BN7Rhjh6RjvUiLKH9yx1X+JMo3XXLTXj2BJ4EDs4sZY4bhFJzWtGuUqUG5uTstbi9jzEBjTJkxpumxPgcYDaxNQqypICnnM75YLJaIYCWJjDF3A+fjzNzgAx7FmVr8FmNMdyAtXnVsnAbyp/Ftd+Pcg7nBWvtVT4L3QEvbKz5bzSbg3zj31je1/6ABHzstN5+vg/Y7F3ihi03L6ua7eCdwCfBNnKLK3UA+MNla2yUGenTZXs8BBrgK52rmz3BmcxvXhca8OMAYswVnitr74q91rBdxSfmTO23IB94Bllhrb0pmvF5TvumOy+/ji8AQ4L/iu98PlFhrZyU/8uRTbu6Oy8/WPJwLClfjFFRuxxkw+yhrbb0X8SdbMs5nNKZTx3QrzpSqLwAR4Angtvi6e3DuqRwWfz0Hp/r4MM49mC8C1yQt0tTQ0vY6F6cr4aXxf02dh9N2XYGbz1dX56atbsEZlO8xnIH43gLO6ioFpzg37XUZzh/+v+PcavcuMLMrFpwOQcd6EfeUP7mjfMAd5ZvuuPl8fR24C3gZSAOeA25IXqie03fRHTftdTFwZ3zbbJxxic7sKgWnuHY/n1FPJxERERERERERSTiN6SQiIiIiIiIiIgmnopOIiIiIiIiIiCScik4iIiIiIiIiIpJwKjqJiIiIiIiIiEjCqegkIiIiIiIiIiIJp6KTiIiIiIiIiIgknIpOIuIpY0zMGHOu13GIiIiIdGTGmIeNMU+3Yf+njTEPJzCkhDHGXGeM2eJ1HCLiXtDrAERERERERKTNbgB8XgchItKUik4iIiIiIiIdnLW2zOsYREQOpqKTiKSCqcaYnwETgI3Atdba+eDcfgecZ619Mf56JjAPyLHWVsa7Wv8W+BpwDGDjzy8CrgNiwB3W2t8n8xcSERERSQRjzHJgrrX2t/HX9wNXAN2ttRFjTD6wC3gMyLLWXmSMuQInD/on8AMgBLwKfMdaWxV/n8uAnwP9gSeBtCY/Mwf4EzALyADeBb5nrd0Qz8WeA74P/AbIjP+cG6y1NfH9RwNzgBOBYuAfwE+ttXUtXH8McC9Obvgh8H5iWlNEkk1jOolIKvgu8AvgKKAAeNIY46Z7+K+Bu4BJQAOwABgGTAf+APzWGHNEAuMVERERSZZXgFOavD4Zp9BzdPz1GcA6IHrQfhOAGcBpwJXAhcDVAMaYk4H/w8mTjgYqgfOa7PsrYAQwM74+Gt++UTfgZuDLwBfjP+OB+HtnAK/h5HSTgctwilf3tHB9Hk6BbEX8Zz+KUzgTkQ5IRScRSQW/s9a+bK3dAPweGAD0cbH/XGvtc9badcBcnETou/HXv8fp7TQ20UGLiIiIJMErwAnGmDRjTD+cC2tv4RSUAM4EXm5mvxBwpbV2tbX2WZxCzpT4uquB56y1D1hrLXAjTuGq0TCgAtgcX/9t4JYm6wPA1dba9621C+L7f80YkwtcCtTj9Fy38fVXA1e2cP1XgTrgOmvtOmvtX4GH3TebiKQCFZ1EJBVsavK8NP6Y6WL/jU2eVwO7G7t3W2sbcBKb9DZFKCIiIuKN93FymS/g9HJaDLwNzIj3DD8deKmZ/SqstbubvC7HKUQBHAl81LjCWhvDuY2t0W9wClTFxphXcXozrWiyvgFY2OT1B/H3NsB4nF5SFcaYSmNMJfAGzrnnqBasPxJYZa2tP+j9RaQDUtFJRFJBQzPLDnV7XXNj0dUf9Prg7uUiIiIiHZK1NgK8iXOL3UxgfvzfdJzbzxrHXDpYuJllvkM8/9T21tpFOL2d/gtnzKXbgYXxW+PAybWa5luN55UNOLnaQpxhDxr/TcQpKK1pwfrPjU1EOhYVnUQk1YWB7k1ej/AqEBERERGPvAKcCpyAU3D6EKdX+A+BNw7qFdQSK4HjDlrWOEYUxpgfA8dZa5+01l6G08vqKJxxosDp1XRkk32PBWpxJnRZi1NA2mGt3Wit3Qjk4Uz8ktaC9SuBicaYpr3Uj0ZEOiQVnUQk1X0I/NAYM9YYcwpwk9cBiYiIiCTZKzhFoiOARfEi0/vAJTQ/ntPh3AOcbYy50Rgz2hjzK5zeRo0GAXOMMccbY4bjzJZXDqxvss1Dxpij47PZ3QX8JT4z3uM4PZ4eMcYcaYyZBvwNyLbWlrVg/dz4+oeMMWOMMbNxxpQSkQ5IRScRSXXXARFgOU5Cc6u34YiIiIgkl7W2EKeH0JLGcStxejz5cApSbt9vEfAVnFntVgDjgL832eRHwHvAs/GfewpwtrW2tMk2c3FmoXsaeAZnMHHihaczgJ44YzG9gHMRcXYL15fhzIY3FFiGc8Hxbre/o4ikBl8sFvM6BhEREREREekA4j2b5gE51tpKj8MRkRSnnk4iIiIiIiIiIpJwKjqJiIiIiIiIiEjC6fY6ERERERERERFJOPV0EhERERERERGRhFPRSUREREREREREEk5FJxERERERERERSTgVnUREREREREREJOFUdBIRERERERERkYRT0UlERERERERERBLu/wM/gPz12dY/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axis=plt.subplots(nrows=2,ncols=2)# 给定一个2行2列的布局\n",
    "fig.set_size_inches(20, 15)# 设置画图域的大小\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "sns.distplot(train['temp'],ax=axis[0][0])\n",
    "sns.distplot(train['atemp'],ax=axis[0][1])\n",
    "sns.distplot(train['hum'],ax=axis[1][0])\n",
    "sns.distplot(train['windspeed'],ax=axis[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x224347afb38>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/A/X5CUeSJEkqrGwS4k2AzWKME/IVjCRJknLEm+oSy6aHOAJ98xWIJEmSVAzZbrt2fQjhYuBdYF7LizHG+3IZmCRJkjqgqfRvdiuUbBLimzJfz23jmgdzSJIkqSQlTohjjNm0V0iSJKmYUvYQJ5VNhRiAEMLaQAAeBJYD3osxWpOXJElSSUqcEIcQ+gA3Az8k3SIxmPQxzoNCCD+KMX6QnxAlSZKUNXuIE8umDeI8oCvQn4X7EP8WmA5ckOO4JEmSpILIJiHeGfh9jHHagoEY4xTgSMBT6iRJkjqRVFNTwR6lLpuEuBcwu43xmiy/jyRJktRpZJPIjgVODSHUZV6nQgjLkt6G7aGcRyZJkqT2a0oV7lHiskmIjwRWAT4HegAPA1OApYCjch+aJEmSlH/Z7EP8MbBlCGE7YO3Me98AHnLbNUmSpE7GfYgTy2bbtUeB3WOMjwGPtRhfNoQwNsa4UT4ClCRJkvJpsQlxCGEY6WowwLbAoSGEr1tNWwtYPfehSZIkqd3KoLe3UJZUIf4cOBaoyjwOBxpbXE8BM4GReYlOkiRJyrPFJsQxxleBQQAhhMeAn8YYp7ecE0LoCmyYtwglSZKkPErcQwycCDwRQlibb+5Okcrye0mSJCmfyuDAjELJZtu10cCnwJ6kD+jYFziBdMvEXrkPTZIkScq/bBLi9YFjYoy3AS8DH8cYzwZ+h/sQS5IkdS4ezJFYNgnxfOCrzPO3gKGZ548B6+QyKEmSJKlQskmInwNGhBCqgfHATpnxdYGGXAcmSZKkDkg1Fe5R4rK5Ee4E4D7gE+Aa4A8hhEnA8sBVeYhNkiRJyrtsjm4eF0JYDegRY/wyhLARsDvpvYpvyVN8kiRJao8y6O0tlKy2SosxzgJmZZ5/BFyaj6AkSZKkQnHvYEmSpDKUch/ixLK5qU6SJEkqO52mQjzvur8UOwRViLoDT2beZ5OKHUbJqes3qNghSJKyYQ9xYlaIJUmSVNE6TYVYkiRJOWSFODErxJIkSapoVoglSZLKURmcIFcoVoglSZJU0UyIJUmSVNFsmZAkSSpH3lSXmBViSZIkVTQrxJIkSWUoZYU4MSvEkiRJqmhWiCVJksqRFeLErBBLkiSpolkhliRJKkdNHsyRlBViSZIkVTQrxJIkSeXIHuLErBBLkiSpolkhliRJKkdWiBOzQixJkqSKZoVYkiSpDKVSVoiTskIsSZKkimaFWJIkqRzZQ5yYFWJJkiRVNBNiSZIkVTRbJiRJksqRLROJWSGWJElSRbNCLEmSVIZSVogTs0IsSZKkimaFWJIkqRxZIU7MCrEkSZIqmhViSZKkctRU7ABKhxViSZIkVTQrxJIkSWXIXSaSs0IsSZKkimaFWJIkqRxZIU6swhPiKuqG70v1cqvA/Pk0PHA9qemfNF+t235valZeg1TDHADm3nERNMwGoLr/ELrsfAhzrji2KJEXl+uWDxMmvsn5l13L9RefXexQJEmqKItNiEMII5J+oxjjpR0Pp7BqBm9AVW0dc8eMonrFQdQN24OGOy9qvl69/ADm/Ot8mD1zkfdV9V6a2o13pKqmptAhdwquW+5dO+ZW7hn7KN27dS12KJKkcuEuE4ktqUL8+4TfJwWUXEJc3X8wjZNfA6Dpw0lUr7Bai6tVVC+9HF122J+qnn2YP+EpGl97Gmpq6TJ8PxoevIGa/U4pStzF5rrl3iorrcgFo07mhNPPKXYokiRVnMUmxDHGgYUKpBiqunQnNXf2woFUE1RVp7/WdWXeS48w/4UHoaqarnseR9PH71G34Q+YN+4BUjOnFy/wInPdcm/4dlsz9cOPix2GJKmMuMtEcln1EIcQlgMGAwv+zbsK6ApsFGP8a45jy7tUw2yqunRbOFBVlU7qAObPZf6LD8P8BgCaprxB9XKrUt1/CHV9lwN2gW496fLjQ2m494rCB19ErpskSSonibddCyEcDHwAPAU8Bjye+Xo/8Mt8BJdvTVPfoWbQegBUrziI1KdTm69VLb0C3fY+IZ3sVddQvfJgmj56jznXnMjcf57N3H+eDXNmVWRS57pJkqRykk2F+ATgImAU8AawJbAMcD1wdc4jK4DGt16iesDadN37RKiChvuvpXbjHUh9+QmN777C/Deepes+J0NTI40TnyH1+bRih9wpuG6SJJUAb6pLrCqVStZfEkKYC6wVY5wUQngAuDTGeFcIYThwQYxxnY4EUn/OQTa6qCDqDjy52CGUpLp+g4odgiTlWlWxA8inL382rGC51dK3PV7Sa5lNhXgG0D3z/C1gfeAuIAKr5TYsSZIkdYQ31SWXzdHNDwHnhxBWA54B9gwhDAD2Abw9XpIkSSUpmwrx0cDfgR8DlwO/BiYD84FDch+aJEmS2s0e4sQSJ8Qxxk+AH7YY2j6EsDYwPcboXVOSJEkqSYkT4kzy25a+IYS+McbXcxSTJEmSOihVAhXiEEItcB6wN+m89Ebg2BhjwxLedwhwRYwxJzfzZdMy8RrpI5pb/uBU5tEEdMlFQJIkSaoYo4CdgF1I55I3AA3Asd/2hhDCKsA5uQwim5vqBgKDMl8Hkj6xbifgRdIfQpIkSZ1FUwEf7RBC6AaMAEbGGJ+NMT4BHAkcFkLovpi3Xg280r6f2rZseojfb2P43RDCDNKHc4zNVVCSJEkqe0OBnsCTLcaeyIwNBZ5t/YYQwq9IHwz3F+B7uQokm5aJbzMXGJCD7yNJkqQcKWQPcQhh+pLmxBj7thpaGZgVY5zRYs5XIYR6oH8bP2MV4K/A9uQ498zmproRbQwvBRwKPJ2ziCRJklQJepAurLY2F+jaxvhVwMUxxlczZ2HkTDYV4t+3ep0i3fT8FHBSziKSJElSxxWwQtxG9TeJ2bSd+HYF6lsOhBAOBlYkfRNezmXTQzwwHwFIkiSpIn0A9Awh9I4xfg0QQuhDunI8tdXcfYE1gS9DCAA1mfkzgUNjjGM6EkhWPcQhhO2A12KMn4YQ9gH2Ap4HRsUY53ckEEmSJOVOCexDPB6YBWwD3JcZ2zYzNr7V3H2BljtPbAdcSfrmu487Gkg2PcTHkr6j7/shhFWB64Bbgf2B3nyzpUKSJElqU4xxdgjhKuDiEML+pM+6uAi4JMY4J4SwFNAlxvhpjHGRinEIYc3M93gnF7Fksw/xb4C9Y4z/AfYDXowx7pN5vk8ugpEkSVJFOR54ELgHuB24i4X3pl0IjCtEENm0TKzEwqB2Jn2SCKR7PPrkMihJkiR1TAm0TBBjnAsclnm0vnbAYt53L4uentwh2STE75Bul/iA9Il1d2XG9wXezFVAkiRJUiFlkxD/Cbg5855/xBgnhBDOI91K8dN8BCdJkqT2KYUKcWeRuIc4xngHsAqwUYxxv8zwDcCQGOMD+QhOkiRJyrdsj27+AqjL7DIBMAPoEULYK8Z4U25DkyRJUrulctZiW/ay2XbtJ8C1wDJtXP4SMCGWJElSyclm27VRpLfF2BT4mvSGyHsBHwFH5D40SZIktVeqqXCPUpdNQjwYOD3G+CLwEtAzxngL6WTYQzkkSZJUkrLpIZ4NLPgd4C1gfeB+4EVgSI7jkiRJUgekmuwhTiqbCvGTwJ9CCH2BF4CfhhDqgGHAV3mITZIkScq7bCrExwJ3AwcBlwNHkU6Eu7DwiD1JkiR1AuXQ21so2VSIJwO3AbNjjPXAZsBrwAPA2XmITZIkScq7bBLic4G9SR/hTIxxFnAREIA/5z40SZIktVcqVVWwR6nLJiHeA9gnxvjQgoEY49+B/YEDchyXJEmSVBDZ9BD3AGa2Mf4lsFRuwpEkSVIu2EOcXDYV4seAs0MIzSfVZXacOAN4PMdxSZIkSQWRTYX4t8DDwNQQwvtAChhAuqd41zzEJkmSJOVd4oQ4xjglhLAuMBxYC2ggfUDHgzFGi/KSJEmdiAdzJJdNhZgYYwPw78xDkiRJKnlZJcR51ZQqdgSlp9rf/Npjxj4HFjuEkrPUmOuY99mkYodRkur6DSp2CJIqVMrUKrFsbqqTJEmSyk7nqRBLkiQpZ+whTs4KsSRJkiqaFWJJkqQyZIU4OSvEkiRJqmhWiCVJksqQu0wkZ4VYkiRJFc0KsSRJUhmyhzg5K8SSJEmqaFaIJUmSylAqZYU4KSvEkiRJqmhWiCVJkspQqqnYEZQOK8SSJEmqaCbEkiRJqmi2TEiSJJWhJm+qS8wKsSRJkiqaFWJJkqQy5LZryVkhliRJUkWzQixJklSGPLo5OSvEkiRJqmhWiCVJkspQKlXsCEqHFWJJkiRVNCvEkiRJZcge4uSsEEuSJKmiWSGWJEkqQ55Ul5wVYkmSJFU0K8SSJEllyJPqkrNCLEmSpIpmhViSJKkMuQ9xclaIJUmSVNFMiCVJklTRbJmQJEkqQ267lpwVYkmSJFU0K8SSJEllyG3XkqvwhLiKuh3+j+rlVoHG+TTcfx2p6Z80X637/t7U9B9MqmEOAHNv+xs0zAaguv8QuvzkUOZcNrIokRdXFXXD902v2/z5NDxw/aLrtv3e1Ky8xsJ1u+OiRddt50OYc8WxRYm8U6iqoueRR1M7cA2Y18DXF5xD07Sp35jT589n0fDs08z5993FibNETJj4Judfdi3XX3x2sUORJJWoik6Ia4ZsSFVtHXP/cQbVKw2ibvs9abj9b83Xq1cYwJxbzoPZMxd5X1XvZajd9IdUVdcUOuROoWbwBul1GzOK6hUHUTdsDxruvKj5evXyA5jzr/PbWLelqd14R6pqKnPdFuiy5dZU1XVhxtEjqF1zbXoeMoKvTz1pkTk9DvgVVb17FynC0nHtmFu5Z+yjdO/WtdihSFKn47ZryS02IQ4hTAYSLWeMcVBOIiqg6v6DaZz8KgBN0yZRvcJqLa5WUb308nTZ8QCqevZh/oSnaHz1KaippcuO+9Ew9npq9j+1GGEXXXrdXgOg6cO21m05uuyw/8J1e+3p9LoN34+GB2+gZr9TihJ3Z1G3zndpeOF5AOa/+Tq1g8Mi17tsvS00NTFv3HPFCK+krLLSilww6mROOP2cYociSSphS6oQn9vi+crA0cD1wPNAA7ARcDBwfj6Cy7eqLt1JzZ29cCDVBFXV6a9dujLvxUeYP+4BqK6m657H0fTRZOo2Gs6858eSmjm9eIEX2WLXra4r8156hPkvPAhVmXX7+D3qNvwB88Y9UNHrtkBVjx6kZs1aONDUBNU10NRIzYCBdN3uB3z9lz/RY5/9ixdkiRi+3dZM/fDjYochSZ2Su0wkt9iEOMZ4yYLnIYTHgBExxutaTBkTQngBOAY4LT8h5k+qYTZVXbotHKiqSid1APPmMv/Fh2B+AwBNU96gevkBVPcfTN3Sy8FWu0L3nnTZ5TAa7r68CNEXz2LXbf5c5r/48KLrttyqVPcfQl3f5YBdoFtPuvz4UBruvaLwwXcCqfp6qnr0WDhQVQVNjQB0/cGOVPfrx1JnjaZ6+RVg/nwaP/6IeZmKsiRJyr1seog3BQ5tY/xFYK3chFNYTR+8Tc0aQ2l8cxzVKw0i9ekHzdeqllmBrrscxpzrT4Wqaqr7D2H+a/9hztUnNs/pfvgFFZcMAzRNfYea1denMY6jesVBpD5deENY1dIr0PUnhzHn76em123lwel1u6bFuo0YXbHJMMC811+ly2Zb0vDkY9SuuTaN701uvlZ/zcI/Tz32PYCmL78wGZYktYu7TCSXTUI8ATgqhHBkjLERIIRQB5wAjMtHcPnW+NZLVK+2Dl33Td/Q1HDfNdRusgOpLz+h8Z1XmD/xv3T9vz9CUyONr/2H1GfTihxx59D41ktUD1ibrnufCFXQcP+11G6cWbd3X2H+G8/SdZ+T0+s28RlSn7tuLTX85ynqNtyYpUZfAlQx8/wz6bb7L2ma9gEN/32m2OFJklRxqlIJb0EMIWwC3A/MAV4FqoD1gUbgBzHGNzsSSP1ZB3ovZLaq/c2vPeoffrfYIZScpcZct+RJalNdv5K731iqJGX9f6TPrbR7wXKrzabdXtJrmfikuhjjOGAwcAbwLvAOcAqwdkeTYUmSJKlYstqHOMb4ZQjhRtKJ8RtAXYzx67xEJkmSpHbzn96TS5wQhxC6ABcAh2SGhgBnhRC6A/vEGGfkIT5JkiQprxKsSYcSAAAgAElEQVS3TAB/BrYEvke6jxjgHGA1SnQfYkmSpHLVlKoq2KPUZZMQ/xI4Isb4DJkqfIzxeeDXwE/yEJskSZKUd9n0EC8HfNTG+FdAjzbGJUmSVCTuQ5xcNhXiJ4HftXidyvQV/xF4OqdRSZIkSQWSTYX4d8DYEMJwoBtwPendJhqBHXIfmiRJkpR/2STEb5M+onkvYJ3Me8cA/4gx1uchNkmSJLVTU7EDKCHZJMQvAfvHGK/PUyySJElSwWWTEK8INOQrEEmSJOVOqrxPps6pbBLiK4F7QwhXAJOA2S0vxhjvy2VgkiRJUiFkkxCfnPl6VhvXUkBNx8ORJElSLjR5dnNiiRPiGGM2W7RJkiRJJSGbCjEhhGrgh8AQ0tuuDQHejDF+lfvQJEmS1F5N9hAnlrjqG0JYEXgFuBk4F1gGOB54I4Swdn7CkyRJkvIrmzaIC4HXgWVZeEPdvsB/gQtyHJckSZI6IEVVwR6lLpuEeHvg9Bjj3AUDmQM5TgI2y3VgkiRJUiFk00NcRfrI5tb64f7EkiRJnYon1SWXTYX4NuC8TC9xCiCE8F3gEuCuPMQmSZIk5V02CfExwEfAVKAX6X7il4HJwMjchyZJkqT2soc4uWz2IZ4J7BVCOAlYK/PeN2KMb+UrOEmSJCnfEifEIYRJwMYxxkmkj25eML4S8EqMcbk8xCdJkqR2sIc4ucUmxCGEXYCtMy9XA04PIdS3mrZGHuKSJEmSCmJJFeLxwFHQ3ByyAYvuKJECZgL75z40SZIkKf8WmxDHGN8nvf8wIYTrgN95TLMkSVLnZ8tEcol3mYgxHggsG0LoAxBC2C6EcHEIYb+8RSdJkiTlWeKEOITwf0AENgkhDAH+DQwFRocQjs9TfJIkSWoHt11LLpt9iE8EjowxPgIcCLwTY9wa2Bs4LB/BSZIkSfmWzdHNA4F7M89/DNyTef4G4JZrkiRJnUhT6RduCyabhPh/wHdDCH2BdYBDMuM7AO/lOC5JkiSpILJJiM8BbgMagYdjjM9mTq07BTgoH8FJkiSpfZpKoLc3hFALnEe6BbcWuBE4NsbY0MbcpYGzSXcqdAEeI70D2tSOxpHNLhNXAlsC+wC7ZIafBraKMf6jo4FIkiSp4owCdiKdW+6W+TrqW+ZeR3pDh12BYUBv4O4QQk1Hg1hsQhxC6NHq+ZvAg0B15vU4YGLLeZIkSSq+VAEf7RFC6AaMAEbGGJ+NMT4BHAkcFkLo3mpuP9KJ8BExxudjjK+S7lDYEFi3nSE0W1KF+OsQwoIb5mYCXy/mIUmSJCU1FOgJPNli7InM2NBWc2cDPwJeaTG24OyR7nTQknqItwe+aPG8vb8ELNEVl8zL17cuW3V5+1+jvN3SlE3rvADmrX9wsUMoSU+Ov4Z5n00qdhglp67foGKHIJWFQp5UF0KYvqQ5Mca+rYZWBmbFGGe0mPNVCKEe6N/qvbOA+1u9/xjgK2BCu4JuYUlHNz/R4vnjHf1hkiRJUkYPYG4b43OBrot7YwhhT9IJ8RExxvqOBpK4VBZCeIy2K8QpoAGYCtwcY3y4o0FJkiSpY5qqCrfLRBvV3yRm03bi2xX41iQ3hHAAcDVwQYzxsnb83G/I5qS6J4CtgY+BOzOPacA2pJPhOcBtIYR9cxGYJEmSytoHQM8QQu8FAyGEPqQrx21upRZCOJr0bhPnxBhH5iqQbJoptyO9L9zfWgX2NLBHjHG7EMLjwGmA27BJkiQVUQncajQemEW6uHpfZmzbzNj41pNDCAcB5wMnxxjPyGUg2STEmwC/amP8EWB05vnzpI94liRJkr5VjHF2COEq4OIQwv5AFXARcEmMcU4IYSmgS4zx0xDCCplrNwHXZF4v8GWMsa1e5MSyaZmIwH5tjO8PvJN5vi7plgpJkiRpSY4nfcbFPcDtwF3ASZlrF5I+8wLSp9P1APYCPmz1GN7RILKpEI8E7g0h7JQJrhrYCFgL2DWEsAnprD2nJWxJkiRlr5DbrrVXprJ7WObR+toBLZ5fTfpGurzI5ujmR4F1SGfx/YFlSfd7DM7sLFEP7BNjPDsfgUqSJEn5kNUJBTHGycCJIYSlga9ijI0trk0EJuY4PkmSJLVDU+F2XSt52exDXAX8PvNYGhgSQjiF9LHNR8cYPWpOkiRJJSebm+qOBX4D/JaFp4r8C/gp8NccxyVJkqQOaKKqYI9Sl01C/CvgsBjjTWT6tGOM95DeZWKvPMQmSZIk5V02PcSrAm+3MT6FdAuFJEmSOokSOJij08imQvwisGeL1wvW+XDgpZxFJEmSJBVQNhXiY4AHQgjbAl2BM0IIawKDgR3zEZwkSZLax10mkssmIf4TsCuwPfAV0B0YC/wkxjg1D7FJkiRJeZdNQrw5MDXGeGqeYpEkSVKOlMJJdZ1FNgnxaODGEMJoYBIwu+XFGOPruQxMkiRJKoRsEuI/Z77+s41rKaCm4+FIkiQpF9xlIrlsEuKBeYtCkiRJKpLECXGM8f18BiJJkqTccZeJ5LLZh1iSJEkqOybEkiRJqmjZ9BBLkiSpRLjtWnJWiCVJklTRrBBLkiSVISvEyVkhliRJUkWzQixJklSGUm67lpgVYkmSJFU0K8SSJEllyB7i5KwQS5IkqaJZIZYkSSpDVoiTs0IsSZKkimaFWJIkqQylih1ACanshLiqiu+fcQD91lqVxob5PHTc1cx4/+NFpnRfpjd73HEKN+5wAo1z51HbvSs/umgE3fr2Yl79XMYedRmzv/i6SB+gSKqqGHbGAfRbO71ujx53NTPeW3Tdui3Tm5/feQo3DU+v2wJ9V1+RX9x9GtduePgi4+Vsy+FbsP9R+9LY2MR9N9/Pvf/vvkWur7zaSpww+jhSKZgcJzP6xL+xybYbs8/hewJQVVXFepuuywHb/4oDjtmPZZZdGoAVVlmB1196g9NG/KXgn6kQth6+BQcetR+NjY3ce/P93P3//r3I9f6rrcTJo48nlUoxKU7m3BMvJJVKcdRpR/DdTdZl9qzZXDLqSl5/+Y3m9/zu1BFMefd/3HHjPYX+OJ3KhIlvcv5l13L9xWcXOxRJ6hSySohDCIOAtYCura/FGG/PVVCFssaOG1HTtY5//vQ0Vthgdbb9497c/avRzdcHfG89tj5+D3r0W6p5bL29h/Hxq5N57sI7Wfvn27DZb3fj8VNvLEb4RTNox42o6VbHv3Y7jeU3WJ2t/rg39x28cN1W3XY9tmi1bgB1vbqz9R/3obGhMhJhgJraGo445TccsvMI5tTP4ZI7L+SZh57li0+/bJ5zxCm/4eqzr+OVZ8cz8syj2HrHLXlq7H94/vFxAOx52C959YWJvP/OlObkt9dSvbjw1vO4+NRLi/K58q2mtobfnXI4B+18GLPr53DFnRfx9EPPLLJuvz1lBFecfQ0vPzue4848mu/tuBXz589nwOqrcPDOv6FP396MHnM2B/3oMPousxR/uvAEVhnUn//37j+L+MmK79oxt3LP2Efp3u0bf41LKjNN7kOcWOIe4hDC74G3gbuAMa0e/8hLdHm20iaB9x6fAMBHL7/L8t8duMj1VCrFbXufyZzpM5vHXr7mAZ6/6C4Aeq/8HWZ9OqNwAXcSK20amJJZt49ffpflWq9bU4q79lp03QC2O+sgnj3rFubPbihYrMU2YPAApr43lZkzZjJ/3nxeHfca391svUXmDFlvCK88Ox6A5x59no232aj52rIr9mPHnw3n+vP/vsh7Dhq5P7dfeyeff/JF/j9EEaw2eAAfvDeVrzPrNmHcqwzd7LuLzFlzvSG8nFm3Zx99nk222YjVBq/Gc0+MI5VKMePLr2hqbGKZZZeme8/uXH3+DYy97aFifJxOZZWVVuSCUScXOwxJ6lSyuanu98BJQJcYY/dWjx55ii+vuvTqTsPX9c2vmxqbqKpZuCRTnnrtG0kdpBO+n910AkMP2IH3HnulILF2JnW9ujP3q4Xrlmq1bv9rY902PXp33n/kFT5/Y0rB4uwMevbqwayvZzW/rp85m569ey0yp6rFb/D1s+rp2btn8+tfHvILbrnqX8xrUVXv+52+bLT1htx/ywP5C7zIevbqwcxvrFvPRSe1WLgF6/b2xHfYbNim1NTWsNKqKzIwrEb3Ht358H8fLdI6UcmGb7c1tbWV3S0nVYqmAj5KXTZ/K1YBt8cYy+FzA9AwczZdenVvfl1VXU2qMdnHu22vv7L06iuy2/XHct02I/MVYqc0rx3rNmT3rZj14Resvecweiy7FLuM+QN3/Lw8e18BfnXcgay3ybqsvtYgXn/5zebxHr26M/OrRX9ZaEotvO2hR88ezderqqrY8gebc9VZ1ywyf9iPv8fDdz5CU1PZ/KfY7JDjDmL9TdZjjbUGMbFFAtvWuqXaWLfnn3yBtYYGLr7lfN55/V3ihLeY8WXl/SuOJCk72VSILwGODyF0yVcwhTbthbdYbbv1AVhhg9X57M3/LfE9mxz+E9bafSsA5tXPTZxAl5MPx73FgO3T67b8BqvzeYJ1+8c2I7njl2dwxy/PoP7TGdy9z1n5DrOorj77On73i5HsOvTn9B+4Er379qa2rpb1N/suE198fZG5b7/2DkO3SK/nZttvyoTnXwVg4JoDmfLOFBrmLNpisvHWG/Lco+MK80EK7Mqzr+XwXxzNj4buTv+BK9Mns25DN1uf11qt21uvvc0GmXXbYvtNeeX5V1llUH++/Gw6v9n9d9x46U00pZqY+dWstn6UJEnNsqkQ3wo8CewdQviIVhXyGOOgXAZWCO+MfYEB26zLHrf/CaqqePDYK9nwVzsx/f2PmfTQS22+Z+I/n2DH8w9jnT2GUV1TzYPHXlngqIvv3bEvsMo26/KzO/5EVVUVD4+8kqG/3onp733Me9+ybpWqcX4jF592OeeOOZPq6mruu3ksn330GQMGD2D3A3dl9Il/45LTL+e4s4+htksd77/9Po/f+yQAq67en2lTPvzG91xl9VWYNmVaoT9KQTXOb+Rvp13K6DFnU11dzb0338+nH33GaoMH8PMDf8q5J17A306/jBPOPpbaLrW8//YUHrv3CWrrath82Kb8ZK8f0TCngXNPurDYH0WSiqbySnbtV9Xynx0XJ4TwMum1vRmob309xnhJRwIZveq+bpeXpTpXrF1uafpmkqnFm+dfq+3y5PhrljxJ31DXr+TqKypdZb0Pw3kFzK1GTvlHSa9lNhXiNYH1Y4xv5SsYSZIk5YZ1s+Sy6SEeB4R8BSJJkiQVQzYV4jHAtSGEm4B3gUVOV4gxlucJAZIkSSXIgzmSyyYhPh6YCfykjWspwIRYkiRJJSdxQhxjHLjkWZIkSeoMvB06ucQJcQhhsafRxRi/sfOEJEmS1Nll0zIxk8XfsFjTwVgkSZKUI+4ykVw2CfF2bbx3dWAk8IecRSRJkiQVUDY9xE+0MfxICOEd4BzgzpxFJUmSpA5pskacWDb7EH+bacDaOfg+kiRJUsFlc1Pdj9oYXop0y8T4nEUkSZKkDnOXieSy6SG+t42xBtIn2B2Rm3AkSZKkwsqmhzgX7RWSJEkqADuIk8umQgxACKEf0B1Y5EDAGOOUXAUlSZIkFUo2PcTfA8YAK7W6VEX6lxD3IZYkSVLJyaZCfAnwErAnMD0/4UiSJCkXvKkuuWwS4jWA3WKM7+YrGEmSJKnQskmIJ5BOik2IJUmSOrmmqiXPUdpiE+JWew/fB1wfQjgTmAQ0tpwbY7wv9+FJkiRJ+bWkCnFbew+PbmPMm+okSZI6EY9uTm6xCbF7D0uSJKncZb0PsSRJkjo/68PJWQGWJElSRbNCLEmSVIbchzg5K8SSJEmqaFaIJUmSypC7TCRnhViSJEkVzQqxJElSGbI+nJwVYkmSJFU0K8SSJEllyF0mkrNCLEmSpIpmQixJkqSKZsuEJElSGXLbteSsEEuSJKmidZoK8UX1E4sdQsmpnz+n2CGUpC7VneaPfcn4Ys7MYodQkrqvtE2xQyhJs6c9VewQSlJdv0HFDkGdjPXh5KwQS5I6DZNhScVgqUySJKkMue1aclaIJUmSVNGsEEuSJJWhlF3EiVkhliRJUkWzQixJklSG7CFOzgqxJEmSKpoVYkmSpDLkSXXJWSGWJElSRbNCLEmSVIasDydnhViSJEkVzQqxJElSGbKHODkrxJIkSapoJsSSJEmqaLZMSJIklSEP5kjOCrEkSZIqmhViSZKkMpTyprrErBBLkiSpolkhliRJKkP2ECdnhViSJEkVzQqxJElSGbKHODkTYkmSJBVFCKEWOA/Ym3ReeiNwbIyx4VvmnwQcDvQG7gKOiDFO72gcJsSSJEllqER6iEcBOwG7AF2AG4AG4NjWE0MII4DfAvsBnwNXAlcDP+9oEPYQS5IkqeBCCN2AEcDIGOOzMcYngCOBw0II3dt4y0jgLzHGB2KMLwD7Az8LIaza0VhMiCVJkspQUypVsEc7DQV6Ak+2GHsiMza05cQQwgrAoJZzY4yvAl8CW7U3gAVsmZAkSVKHhBCW2McbY+zbamhlYFaMcUaLOV+FEOqB/m3MBZjaavzDNuZmzYRYkiSpDJXAHhM9gLltjM8FurYxd8G1Jc3NmgmxJEmSOqSN6m8Ss2k7me0K1Lcxd8G1r5cwN2v2EEuSJJWhJlIFe7TTB0DPEELvBQMhhD6kq8GtWyM+yHxdsdX4im3MzZoJsSRJkophPDAL2KbF2LaZsfEtJ8YYPwImtZwbQlgP6As829FAbJmQJElSwcUYZ4cQrgIuDiHsD1QBFwGXxBjnhBCWArrEGD/NvOVvwOkhhPeBj4CrgFtjjFM6GosJsSRJUhkqkaObjwe6A/cA84ExwEmZaxcCw4DVMq//BvQDrgfqgHtJ72PcYVWp9u8dl1OD+m3QOQIpIfXz5xQ7hJLUpdrfA7P1xZyZxQ6hJM2Z3+bJo1qM2dOeKnYIJauu36Bih1CKqoodQD7tNWC3guVWN71/Z0mvpZmBJElSGSqRo5s7BW+qkyRJUkWr+IR4+x2/x50P/YN/3X8De/zfT7913sl/GcneB/y8+fW239+K28bewG1jb+D0s08oRKidyvAfDmPso7dw74M3sc9+v/jWeaeNOp79Dtyj+fVvjjiQBx7/F2MfvYWdfvyDQoTaqfxgx2255+GbuOOBf7DXfj/71nl/OuM49j0gva5rrxv4593XNj/emvYC236/w6dUloydfvR9nnjqTh557DYOOHDPb1xf77tr8eBDt3D/2Ju4864bWG65fgAcfsRBPPbEHTz2xB2ccOJvCx120f145+E8+8y/efrJuzn4oL2/cX399dfh8Udv55GHbuW+e8c0r9vo80/nuf/ezyMP3cojD91Knz69v/HeSjVh4psccMRxxQ5DSqwEtl3rNCq6ZaK2tpaT/zyS3Ybvy+z62dx63/U88sCTfPbJ581zlvnO0px7yZ8ZuPqqTHrn7wD07NWD4089ir13/TVffjGdQ47cn2W+szRffP5lsT5KQdXW1nL6qOP54Xa/pL5+Nvc8MIYHxz7Gp5981jznO99ZmosuP5NBa6zGpW9PBqDPUr05+NB92WLDH9KjR3ceefoO7r/34WJ9jIKrra3lT2ccx0++vxf19fXcfv+NPDz2cT5t9edt9GWjGLT6AK7IrNvrr0X22OUgAHbedQc+/uhTnnjkP0X5DIVWW1vLmWedzLbb7MqsWbN5+NFbue++h/nk44V/1s455xRGjjyFVye8wUEH78XRxxzKFZf/nT323JVh3/spqVSKBx++hbvvfpCJr71ZxE9TOLW1tZx7zilsvuXOzJpVz5NP3Mm9/36Ijz/+tHnO6PNO43dH/5Hx4yfy61/ty3HHHs6xx53Ghhusx4923pvPK+Tvs6SuHXMr94x9lO7dOnwglqRO6FsT4hDCZBKe+hdjLMlO/jWGDOT9yf/jqxnpA09eeO5lNtl8A+6/e2GS1qNndy48+3KG/WBhRW7DTdbnrTfe4cTTj2HVASvzz3/cWTHJMMDgMIjJk6YwY8ZXADz335fYfIuNuOeuB5rn9OzVg3POvITvD1+4tWD9rNl88L9p9OjRnR49u9PUVFndTWsMGcR7kxeu27jnXmbTLTbi33c92DynZ88ejD7rUrb7wdbfeH/3Ht055vgR/HznAwoVctGFNddg0qT3mT49vWbPPvMCW225KXfccV/znP33P5KPP0onerW1tcyZO5cPPviQn+56QPOfsbq6OubOaet00PK01lqDeffd95g+fQYAz/xnHFtvvRm33XZv85y99x3BRx99AkBtbQ1z5s6lqqqKNdYYyOWXnc3yyy3LtdfdxPU3/LMon6GzWWWlFblg1MmccPo5xQ5FSqxEdpnoFBZXIT63xfOVgaNJb3PxPNAAbAQcDJyfr+DyrVfvnnz99cK752fOrKd3q38e/GDKND6YMm2RhHiZ7/Rl8602Zuft9qR+Vj3/vOdaXn5hPJPf7fA2eCWhd+9efP3VwnWbNXPWN9ZtyvtTmfL+1EUSYoBpUz/iyefupaammr+Nvqog8XYWvXv3XGTdZs6cRe8+vRaZ878pU/nflKltJsR77vtT/n3Xg3z5xfS8x9pZ9Ondq/kXVkivWZ+lFv2ztiAZ3myzDTn00P3YcYc9mD9/fnOF84xRJzJh/ETeeWdy4QIvsj69ezHjq4Xr9vXMmSzV6r/RBcnwFptvzIgRB7Ld9rvTs2cPLrn0OkZfcAU1NTU8/NCtvPjSBF599Y2Cxt8ZDd9ua6Z++HGxw5CUJ9+aEMcYL1nwPITwGDAixnhdiyljQggvAMcAp+UvxNw75oQRbLz5Bqy59mBeefG15vFevXrw9YyvF/POtC+/mMGEVyY2t1Y8/+xLrLVuKPuE+A8n/Y7NttiQtdYZwssvTGge79mrJ19lqp6Ls/3wbVh+hWXZdP107/DNt1/NuP++xMsvvZq3mDuDY088kk023yC9bi8u/Ky9evVcJNlbkt1+sTOHHXBMPkLsdP50yki22GJj1ll3TV544ZXm8V69ejJj+jf/rP3sZzvz++OO4Ge7H8Rnn30BQNeuXbjs8rOZOXMWR/3ujwWLvZhOP+04ttpyE9Zbby2ef/7l5vHevXoxvY3/Rn/xi1044fgj2WXX/fjssy+orq7mb/+/vfuOr6JK/zj+ISR0BMSOAir4ACrYEH9gQVR0XV11146oKC6grmuva8HCuiKgq4IKKsUC6uqqCGJZQBFRLIBSHgTBQlGUIoQakt8fZxJCJDDB3HuT3O/79coruXfKfebkzMxzz5w588gg1qwJQzqOG/chLVu2UEIsUk6l13XY3yduH+LDgW5beP8zoHnphZMcff/ZHwiXV9+e+B/q1N2B1dmraf1/hzDw0aHbXP6rqTPZr1kT6u1Yl19XrOTgww5kxLBXEh12yv3rvoeBUG7vfzySunXrkJ29miPaHsaAR57e5vIrlv/K2jXrWLcujM26YsWvv2ntq4ge7PUIEMrtvY/+W1Df2vzfoTzx6OBY66hduxZVqlRh0YL0aKG6u2cfIJTZp5+/Tb16dVi1ajXtjjychx/e/MrCOeeezqWXns8fTjqXZctWFLw/4sWBjB8/kX59n0hq7Kl0x50PAKHcvpw6jnr16rJqVTZHHtWGPv0e32ze88//M3/tegHHHX8Wy5aFqw777bcPzz07gNaHn0hGRgbt2h7O0GEvJX07RESSLW5CPA242sz+5u4bAcwsC7gFmJyo4BItJyeHe2/vw5CX+lMpoxIvP/caPy5eQpP99uHCrudwx43/3OJyS39ZRu97H2HwS6ERfdR/32H2rLnJDD2lcnJyuPO2+xn+ykAqZWQw/NlXWLzoJ/azfbnksk7cfP3dW1zu448+Y0r7Lxn17nByc/P4ZNJnjB87McnRp05OTg73/KM3z778BBkZGYx47lV+XPQTTW0fLup6Hv+44b5il927SSN++G5hEqMtG3Jycrjl5vv47+tDyMjIYNjQl1i08EeaNWtCt+4Xct21d9H7wTv54YeFPP9CSPgmTPiYadNmcORRbahatQodO7YH4M47Htis1bQiy8nJ4YYbezLqzefIyMhg8ODhLFy4mObNm3J5jy78/ep/8FDfu/nu+4W8/GL4gvH+B5PoeXcfXnjhFT6c8AY5G3IY9uzLzJgxO8VbIyLbq6w8fK08iPWkOjNrDYwG1gJfEp7s0grYCBzv7r/71m09qa7k9KS67aMn1ZWcnlS3ffSkupLTk+q2n55Ut13K9dPVtuWMhqcmLbd69bs3ynVZxsoM3H2ymTUFzgX2j95+BRju7tvuPCoiIiIiSVURxgdOlthNZe6+zMyGAU2BmUCWu8e/I0hEREREpAyKlRCbWRXgIeCv0Vv7Af8ys+pAJ3dfUezCIiIiIpJ0GmUivriPbr4HaAscTehHDNAbaEw5HodYRERERCRuQnw2cKW7TyR6ep27fwJcBpyaoNhERERERBIubh/iXYDFW3j/V6BG6YUjIiIiIqVBj26OL24L8fvA3wu9zov6Fd8OTCj1qEREREREkiRuC/FVwBgzOwGoBgwmjDaxEeiYmNBEREREZHtp2LX44o5D/LWZNQfOB1pEyz0HPOvuqxMYn4iIiIhIQpVkHOJ1ZvYSm49DrGRYREREpAzSo5vj0zjEIiIiIpLWNA6xiIiISAWUm8Sf8k7jEIuIiIhIWtM4xCIiIiIVkMYhjk/jEIuIiIhIWtM4xCIiIiIVkMYhjk/jEIuIiIhIWtuecYiN0DLs7r4mYZGJiIiIyHbTOMTxxR2HuBrwGNAJyIreXmtmA4Hr3H1jguITEREREUmouC3EDwPtgXOATwg347UBHgTWATclIjgRERER2T7qQxxf3IT4bOBUdy88osQrZrYUeBElxCIiIiJSTsVNiNcAG7bwvh7ZLCIiIlIGaRzi+OImxLcAg8ysGzDJ3XPN7ACgP3CfmRU8nEOjTkCell8AAB9fSURBVIiIiIhIeRI3IX4IqAV8AGw0s1zCzXWVgMOBvoXmrVyqEYqIiIiIJFDchPj0hEYhIiIiIqUqV8OuxRb3wRzjC7+OHtvcEpjt7r8mIjARERERkWSIOw5xE+ApwmgS04CJhIR4hZn9wd0nJS5EERERESkptQ/HlxFzvkeAlcB8oDOwJ+GJdQPYvP+wiIiIiEi5EjchPgq4xt0XE/oTv+nuXwMDgYMSFZyIiIiIbJ9c8pL2U97FTYjXAllmVhM4Bhgdvb8bGotYRERERMqxuKNMjCG0Bq8EVgNvmNlxhEc6v56g2ERERERkO1WElttkidtC3A34lNBS/Ed3zwZaA+OAaxITmoiIiIhI4sUddm0V8Pci792fkIhERERE5HfL0zjEsRWbEJvZ03FX4u6XlE44IiIiIiLJtbUW4tqF/q4CnArMAiYD64FDgFbA86URyPTH/1Iaq0kvGXF7vMhm6uyU6gjKncxWx6c6hHKpXcsuqQ6h3Km15zGpDqFcWvXDeDb8/E2qwyh3snbaJ9UhJJT6EMdXbELs7mfl/21mTwIPufu1hecxs3uBZokLT0REREQkseKOMnEeoUW4qCHAlNILR0RERERKQ55aiGOLe819MdBhC++fAnxbeuGIiIiIiCRX3Bbiu4GnzKwD8DlQCWgDnAyctbUFRURERCT5NMpEfLFaiN19GHAikAd0Bi4A1gBHu7sezCEiIiIi5VbcFmLcfSwwNoGxiIiIiIgkXayE2MyqA92BQ4EsQpeJAu5+dumHJiIiIiLbS8OuxRe3hfhJ4M/AW8CviQtHRERERCS54ibEJwHnu/triQxGREREREqHbqqLL+6waxuA2YkMREREREQkFeImxP2A+81s50QGIyIiIiKlI5e8pP2Ud3G7TJwNtAQWm9lKYH3hie6+S2kHJiIiIiKSDHET4kcTGoWIiIiIlCo9ujm+WAmxuw9JdCAiIiIiIqlQbEJsZi8CXd39VzN7CYr/mqFxiEVERETKllyNMhHb1lqIs9mUBK8GNlLkgRwiIiIiIuVdsQmxu3cp9LIzMBMYBbwJfODuGxMcm4iIiIhsJ/Uhji/uTXW7Ax2BE4ERQFUze4eQHL/l7osTFJ+IiIiISELFvanuR2BY9IOZHQJcBzwVzVI5IdGJiIiIyHZRH+L4YiXEZpYBHAIcVeinOvAeMD5h0YmIiIiIJFjcLhO/AtUICfD/gN7AZHfPSVRgIiIiIrL91Ic4vriPbh4COHAkoS/xCUA7M6uaqMBERERERJIhVkLs7le4+/7A3sCThJvs/g0sNbNxiQtPRERERCSx4naZyLcUWAAsBlZEy9cp7aBERERE5PfRTXXxxb2p7kagA9AO2AC8CwwGznH3RQmLTkREREQkweK2EJ8HjAbuAybqoRwiIiIiZZtuqosv7jjEByc6EBERERGRVChpH2IRERERKQfUhzi+uMOuiYiIiIhUSGohFhEREamA1Ic4PrUQi4iIiEhaUwuxiIiISAWUl5eb6hDKDbUQi4iIiEhaUwuxiIiISAWUqz7EsaV1Qpybm0ev1z9m9uJlZGVW5s4zjqBh/R0Kpk/wBTwxdhoAzXbfkVv/dDhrN+Rwy4gJrFiznupVMrn3rHbsWLNaqjYhJXJz8+j12iRmL1pGVmYGd/65LQ13KlxuP/DEe1MBaLZHfW49rU0ot+EfsGLNOqpnZXLv2UexY600LLfh7zJ7wZJQ3zp1pOEu9QqmT5g+jyfe/AiAZnvtwq3nHsczb3/ChzPmA7ByzTp++TWb9+7vkYrwUyI3N5d7HnyM2XO+IatKFnfffDUN99yjYPpTz77IqHfGU6tmDbp0OpP27dqw4teV/PHcrjTZpxEAxx3dls5nn56qTSgTjjyhLV2vuYiNORt5ffgoXnt+5Bbnu+auK/h27ve8Muz1JEdYdvzx5OO59darycnJYcjQETz99AubTW/ZsgX9+t3Dxo0bWb9uPZdcejU//fQz3btdROfOZ5GXl0evXg8xavR7KdqCsmfa9Fn0HfA0gx99INWhiBQrrRPisTO/Z13ORoZ2/wPTvltC31Gf8VDnYwHIXreBfm99xqCuHalXsxrPvD+dZavXMWrKPJo3qE+3Di157fO5DBz7JTed0jrFW5JcY2d8F8rt8pOjcvuUhy7sAETlNvozBl12Yii38V+xLHsdo6Z8E8rtuFa89tkcBo6dxk2nHp7iLUmusVPnhHK74XymzVtI31fG81D3kKhlr11Pv1fGM+ias6lXqwbPvP0Jy1at4ZIT23DJiW0A+Fv/V7n69KNTuQlJ9977H7F+/Xqee7IfU7+aSe9HBvLIv+4EYPbcebz5zjheePIhAC7ofi1tDm3FDJ/Dyccfw63XXp7K0MuMypmVueauK7j45G6sWb2WQa89xoR3JvLLkqUF89TdsQ53/fs2Gu6zJ98OGJ7CaFMrMzOT3r3vpG27U8jOXs24sa/y5pvv8uOPSwrm6dOnJ9dcczvTps2ga9dOXH/d5fzrgUfo1q0zrQ8/iWrVqjJlyv8Y1aRNCrek7Hj6uZd4463/Ub1a1VSHkpbyNA5xbLH7EJvZN2ZWfwvv72FmP5VuWMnxxbc/0W6/0NrUsuHOTF/wS8G0qd8toelu9egz+jO6PDmG+rWqsWPNalzQrjld2x8AwOLl2dRPs1ZOgC/m/0S7/RoA+eX2c8G0qd/+RNNd69LnzU/p8sToUG61qnHBkS3oeuyBQBqX29wFtGvRGICWe+/B9G9/LJg29ZuFNG2wE33+M54ufYZTf4ea7Fi7RsH09774mh1qVKVttHy6+GLadNodcSgArQ5ozvRZXxdM+2b+97Q+uCVVq1ahatUqNNyzAbPnzGOGf82M2XO5+IobuPYf97Hk56XFrT4t7N20ET/MX8DKFavI2ZDD1E+mcVCblpvNU6NmdQb2eYbR/3k7RVGWDc2aNWHu3PksX76CDRs2MHHiZNq12/yLe+fOVzBt2gwAMitnsnbdOn75ZRmHtT6RnJwcdtt1Z1Ys/zUV4ZdJe+2xOw/1+keqwxDZpq22EJvZn4Ajo5eNgZ5mtrrIbE0SEFdSZK/dQK2qVQpeV86oRM7GXDIrZ7Asey2Tv1nMiCtPoUaVTLoMHEOrhjvTaKcdqJyRwWVPvc2cxcsZ0OX4FG5BamSv20CtalkFrytXyihUbutCuV31p1BuT7wVym3nOqHcBo5hzo/LGHBJxxRuQWpkr11HreqbWkk2q2+r1jB59veMuOVCalTNokvf4bTae3ca7bojAE+N+Zj7L/ljqkJPmVXZq6lds2bB64zKGeTkbCQzszJN923MoGEjyM5ezYacHKZ8NYOzTjuJvRvtRQtryv+1PpiRY/5Hr3796Xdf+p6Qa9auyaqV2QWvs7PXUGuHmpvNs/D7xSz8fjFtO6R3q+YOO9RmxYqVBa9XrlpFnTq1N5tn8eLQ/nPEEYfSo8dFHHf8mQBs3LiRHt0v4vbbr+Ox/k8nL+gy7oRjj2TBoh+3PaMkhPoQx7etLhNTgauBStHrg4H1habnAauAi0o/tMSrWS2L7PUbCl7n5kFm5dBoXrdGVfZvUJ+dalcH4JDGuzJr0VIaRX1lB17akXlLVvC3of9j5HVnJD/4FKpZNYvsdTkFr3Pz8jYvtz132lRue+/KrEXLaLRzHQAGXnYi835awd+GvMvIG/6S/OBTqGa1qmSv3bT7bFZuNauxf8Pd2KlOSFQOabIns35YQqNdd2Tuol+oXaPqZv2N00WtmjXIXr2m4HVebi6ZmZUB2LdxQ877y5/ofv3tNGywBy1bGHXr1OHA5ka16PLscce05dFBw1ISe6p1v/FSWh1+IE2a78v0L2YWvF+zZnVWrliVwsjKnrvuuoG2bVtz4AHNmTz5i4L3a9eqxfIttPaeeeap3HzT3zj9jIv5udAViAGPD2HQU8/zxutDOeaY/2P8+I+SEr+I/H5b7TLh7t+6ewd3PxYYAvzB3Y8t9NPB3f/k7qOTE27pOqjhzkzwBQBM+24JTXetWzCteYP6zPlpOcuy15KzMZcvv1/CvrvU4anxXzLyi28AqJ6VSUalSltcd0V2UONdmOA/AFG57bYpUWveoD5zfixUbt9F5TbuS0Z+PheA6lUyyaiUfiP+HbTvHkyYPg+AafMW0nSPnQqmNW+4K3MW/cyyVatDuc1fxL67hx5KH8/6liP33zslMafawQe24IOPJgMw9auZNN13UzksXbac5StWMGxAH26+ujuLf/qZpvs04o77H+adcR8C8PGnU9i/WdOUxJ5qjz/wFD3OvJqTWp3Ono0bsEPd2mRmZXLQEa348rPpqQ6vTLnrrt507Hg2ezU8mH33bUy9enXJysriyCMP5+OPP99s3vPOO4MePS7mhI5nM2/edwDs13QfRgx/EoANGzawbt16cnPVMidSnsS+qc7du5hZppk1AbLY1GqcP31GaQeXaB1aNGTSnEVc+MRbkJdHz7+0ZdiEGexVvzbtm+/FVR0P5vLB4U7hjgc2osmu9ahXsxq3vzyRVz+bQ25uWCbddGjRkElfL+TCAaMgD3qe2Y5hH0wP5daiIVedeAiXP/0OAB1bNqbJblG5vTSBVz/9mty8PHqe2S7FW5F8HVo1ZdLMb7mw9/MA9Ox8IsPe+5S9dq5L+5ZNuOq0o7j8kf8A0PFQo0mUMM//cSlHNGuUsrhT6bhj2jJx8hd06nYt5OVxz23XMmT4KzRssAftj2zDDwsXc86lV5GVlcV1V1xK5cqVuaZHF27v1Y/hr46kerVq3H3z1anejJTamLORh3o+xr+ff5BKGZV4Y/goliz+mb2bNuKsLn/mgVv7pTrEMiMnJ4cbb7ybkW88S0ZGJYYMeZGFCxfTrFlTLu9xMVdfczt9+9zN998vYMSIkAB/8MEk7rmnL9O+nMH7418jLy+PMW+P5YMPJqV4a0R0U11JVIpbWGb2R+AZoD5FkmEgz90r/55A1rx8r/5rJZWRfq2spaLOTtueRzaT2Sr9+sqXhnYtu6Q6hHJn6tJvUh1CubTqh/GpDqFcytppnwp9mbdBvf2TllstWDa9XJdlSYZd6wW8D/QEdAutiIiISBmWqxbi2EqSEDcBznH3WYkKRkREREQk2UqSEH8CHAgoIRYREREp4/I07FpsJUmIXwWeNLNjgdlsPvwa7t6/NAMTEREREUmGkiTE1wDLgT9EPxDGIa4U/VZCLCIiIlJGaJSJ+EqSEDcF7gQWuPvjAGb2MfAmcF8CYhMRERGRNBc9Ofl+YG/gU6C7uxc7oLqZXQLcADQk9Gq43d1Hbu0zSjJuV2+gEzC30HuPEZ5Sd3cJ1iMiIiIiCZZLXtJ+EsXMWgEjgEeBQ4F5wFtmVr2Y+U8n5Ke9gJbAUOBVMztsa59TkoT4PKCTu7+T/4a7DyUkxBeXYD0iIiIiInFcBYxy9/7RQ+AuJTwg7sxi5u8KDHb3Ye4+1937AeMIjbrFKklCXB1YtYX3lwF1SrAeEREREUmwvLy8pP0kUDvCczAAcPcNwETgyGLm7wn0LfJeHiGPLVZJ+hCPBR4ws07uvhTAzOoS+g+PK8F6RERERKQCMbPl25rH3etux6obAAuKvLcIaFzMZ0wuEldLoAMwZGsfUpKE+CrgXWCBmX1LyLYbAXOA00qwHhERERFJsOQ+qW77ntxsZgcAXxYzeQhQA1hX5P11QNUY694deAX4mNAPuVixE2J3/y4K+gSgOWEc4tnA2+6eG3c9IiIiIlKxbGfrL4Rcsnkx01YQ+goXTX6rAqu3tlIz2xt4G9gAnO7uOVubvyQtxLj7esIwa2+WZDkRERERSa7yMA5xlFsW+xRkM/sB2L3I27vz224UhZdpQejV8BPQ0d2XbCuOktxUJyIiIiKSTBOBo/JfmFkW0DZ6/zfMbE9CMvw9cKy7/xTnQ0rUQiwiIiIikkSPApPM7GpCF4ibCX2IXwIwsyrAjsDSqLX534Rh2boAVc1st2g9a9292Bv/1EIsIiIiUgFVhAdzuPvnhDGEryQ8pa4RcJK7r41maUsYdaKtmVUlDPSwEzA9ej//Z9DWPkctxCIiIiJSZrn7S0QtwluYNo7Nh7iovD2foYRYREREpAIqDzfVlRXqMiEiIiIiaU0txCIiIiIVUHIfzFG+qYVYRERERNKaWohFREREKqC8BI7+UNGohVhERERE0ppaiEVEREQqIPUhjk8txCIiIiKS1tRCLCIiIlIBaRzi+NRCLCIiIiJpTS3EIiIiIhWQRpmITy3EIiIiIpLW1EIsIiIiUgGpD3F8aiEWERERkbSmhFhERERE0pq6TIiIiIhUQOoyEZ9aiEVEREQkramFWERERKQCUvtwfJXUnC4iIiIi6UxdJkREREQkrSkhFhEREZG0poRYRERERNKaEmIRERERSWtKiEVEREQkrSkhFhEREZG0poRYRERERNKaEmIRERERSWtKiEVEREQkrSkhFhEREZG0poRYRERERNKaEmIRERERSWtpkxCbWUszOzbVcZRnKsPyw8zmm9mVqY5je5lZezPLM7NaMeZtbGanJSOuQp95pZnNT+Znbk1UVqekOo5UM7PBZvby71j+ZTMbXIohlZqyVudKQyr2XZHipE1CDLwG7J/qIMo5laEky0RgdyA7xrzPAEclNhwpJ/4OdE11EBKb9l0pMzJTHUASVUp1ABWAylCSwt3XA4tjzq56KQC4+4pUxyAlon1Xyoy0SIjNbBzQCHjEzM4EzgX+DfwBWAW8CVyXfzA1szzgPOAWwIBPgQuAG4DOwK/ALe4+LJp/frS+s4CDgCnAFe7+eVI2sBSZWWvgfqANoX5MA64GelGoDN29vZnthsoRM2sEPAx0ANYAw4HrgcZAH+BooDrwNXCru78eLXcGcA/QBFgE9Hf33tG0+cCD7v5o9LoxMA840N2/MrNdgb5AR6AO8D3wT3cflPAN3oJC8d0OXAN8DPSMYjw0im8gYZtyo2U6RNPz68b/gGOiutUeGAvUdvdVZvZX4EZgr+hzern70Ojy9jHAMVG9bGxmtQnlfiaQF6337+6+MPrcPOBe4K+EpPsQYF9C3TsaWAKMAG5393XRMq2BR4CWwGRCC3ZZc5iZ3UGIcQ5h3xkPBdt8qruPjF63Z/PynU/Y7zsBrQGP/j4TuJJQjr3d/YFkblA+M5sCDHf3+6PXjwEXA3XcPSc6Fi0ChgE13P1MM7s4iv1FQp3MAt4Curl7drSezsCdhKsRzwNVCn1mbeBx4CSgGvAB8Dd3/zoqv/8Sjo3/JOzfLxLq2Zpo+f3Yep3a1vTyUOeArR4Dvwb6AWcAhxPq5W3u/saW9t0UhC5SIF26TPwZ+AG4Nfr7FcK2/x9wKuFkOLzIMvcTDnZHAA2BzwkJXOto+SeK9G+8h3AwPoRwMnnbzHZM0PYkRLQ9owmJaCvCtq8EnuC3ZQgqR8ysKvAO4YR4NKFsTgPuAt4glN8RhAT/S+AZM6sSJbQjCCcLI3xJuM/Mjov50UOBXYDjgBbA68CAKDFIpVMI9eFfwBhCAnIgcBVwBSGpxcz2JnyBGkUom5cJdes3zOwQQmJwLZCfRAw2s6aES+QfAQMIdQrgSUKZnkg44eYBY8yscANAJ8LJ+yJCojQG+AY4mPBl7STCCZ6o/r0FTCXUy6GEBKus6UGodwcStuV5MytJC9x9hC8SBwEbgfcJX+qOJHxxud/MmpRivCUxmvD/yncsYZ87JHrdEZgF5BZZriXhkvzxwGWE/bM7QHQ/xNOEbTuE8KX+1ELL3gvsA7SPpudG8+erRdhv/wL8KfqM/tG6q7H1OrWt6eWlzm3tGHhnNMvdhC8WhxES5GfMrApb3ndFUiYtEmJ3X0o4wK8kJHotgU7u/pW7f0o4OZ5kZgcUWuwxdx/r7lOAkYSD5a3u7oQDaHVg70LzD3f3/u4+E+gG5ADnJHrbSlkN4AHgJnefG217f6BF4TJ096XRyUTlGE6CjYAL3H2Ku39IiHshMIjQojQr2p4HgR2BXYE9CInY9+7+rbu/TEhuv4r5uW8SWrqmufscwsk7E2haitu2PR5y99mE5GWyu9/j7l+7+2jClYLro/kuA2a6+60ePAz8p5h1NiIkI99GZdWfkOwuia5GrAdWu/sSM9uHcAXofHef7O5fEZKNxoSEI9+T7j49qpfnARsILaru7u8TkqbLzGwHQv1bB1wZ/S+fAgaXQlmVtn+5+yh3/5qwH+8B7FyC5Ye7+3/dfRbhi20toEf0+gHCF4vmpR10TKOBdoW+TDYG3mNT/9MTCV+uisoCLov+168SksxDo2ndgf9GxxsnfOGaVWjZxoRzxrxoelfgpkLTKwPd3X1iVGeuBTpFdWZbdaqi1Dko/hi4IJr+grsPd/cZhKtG9YHGRffdVAQuUlhadJkoYn9C4veLmRWdZmxKSOYUen81MN/d86LXa6PfVQvN80H+H+6+zsymAYUTwzLP3X8ys0HAFWbWitAadzBb/uKkcgxaEBK1ggO6u48htEjWBM43s8MIZZnfmlWZ0Ar/XDTfN4QEd6i7/xjzcwcAZ5lZfqvpwYXWnUpzo9/7A+3NbFWhaRlAdTOrz6bLwIV9RLi0WtRbwIfAFDObTvhi9Yy7L9/CvC2i316kXtYg1MuRReLMj3UfYGWhZSpF8TYl1L8v3X1DoWU+YfMEuywovE35ZVO9BMsX3VcX51/+d/eNZraBzffVZJpISCCPICT6HxO6whxlZn2BEwhfhC4sstxKdy/cF/1XoGb09wHAs/kT3D3PzArXyX8S9sslZjae0EViWKHpGwl1Nt8nhATc2Hadqih1Doo/BmJmNwGzC837a/Q7K3nhicSTjglxJvAt4QBaVOFkZEORaUUvxRWVU+R1BuGAWW6Y2e7AZ8BMQovM84TL8s9tYXaVY7Ce0HK2mSgZ/pjQn+5VQpeGVcA4CCdf4AIze5BwefFk4HIz6+KhT3XRdRbsq9Fl8DGExGA4oaVsOqGLSaqtiX5nElp8/7GFeVYQ6kWsK1TuvsbMTgDaEi5pnwL83cxOcff3isyeGa37YH5bhku3EGf+Mh8BXbbw8fmtXEW7HqyPE3uSbWk/Ka7LxJaO/SXdV5Mm6if8LuHKw27A+OjnOsIXzfw+vkUT4i39nyoV83f+/JnRZ06K+safSrhPohdhHz08mjeXzcsovz5vZNt1qqLUOSjmGFhkelG6mU7KnLToMhHJ32FnEhKJle4+J7rcvJ7Qd26X37H+/Na//P5hLQn9v8qTPxPK4nh3f9Dd3wX2hIIkrPBBT+UYzAYaFe7nbGYXEhLfpsBR7t7L3d8kdJUAqGRmrcysb3SJsae7tyHclHNeNM96ws1y+fYp9HcLQh/Kk939ruhScN38dZfy9m2vmUCz/LoR1Y/mwB2EJOIrNl26zrfFfoTRDUy3ufuH7n6zux9A6Iv+l2iWovUyC6hZ6HMXAb0JLenFxdoU+KHQMjsS+r9XIdxY2irqK5nvkN+upkzbWn0qL0YTuhW1IyTDkwkt4NcB7xRpTY1jGuHm4cIKH39uAdq4+/Pu3pnQOn0g4ZgEoZ4Vvnp1OOGql7PtOlWR6twWj4Fm9kmMZbeWSIskVTq1EK8inJBfJLSmDTez6wktko8QDkbzf8f6u5nZp4QT9a2E1pYXf0/AKfALIWn7o5l9RbiZJL+FrypRGZrZLoSbKFSO8DbhUvNgM7uVkJjeRThJVAHOiUY5OQR4KFqmKqG1soeZLSNctt2DcMLNvyQ7mVAWo6L13Mumk8dyQivUuWb2HJtuNMtfd1nwGHCVmf07+nsvws2Zr7l7rpk9DlxvZvcBQwgtf+cAE7awrtXAnWb2I6G8mxH25fwRNVYBTcysgbu7mb0ODDWzKwh3799HKNtZv101EMr/DmCImd0D1I7W/Z27rzCz4YQbhAaaWS/C/7Irm7c4l3WTgevM7HPCiArXb2P+smg0oavQRmCSu28ws4mErhKXbcf6HgY+iLodjSS0LrciJKMQGgM6m1lXwpeqiwmX/GcTbjyEUCd6ADsQGgMGuXu2mW2rTlWkOlfcMXAoocy2pvC+u2Ab84okVDq1ED9MuLlmDOGO4KWEYYfGExLBk93991yaf4pwU8XnQAPgOHdf+bsiTr4XCUNjDSacFLoRTjR5hNa8gjL0MHRW2pdjtK2nEb5cfgK8RCjHU4DbCC0+Mwgnv+uBZcCh7v49oUX+DMIXi/8Q+ijeF636NsKJ9yPCieUuosuz0Ynjr9HPLEIy3J/wPyva6poS7v4D4Uanwwgt/MMIo2pcHU1fQCi30wmjb5wXzfOby6vu/glwKaFeOKGO9nH3Z6JZBhBaDaeaWQZh5IhPCeU5mdAyekIxfY7xMARXR6Ae4X/4RrTc+dH0FWy6cegLwv+x33YVTOpcSfjSOoWQuN2c2nBKzsOweTOBT/P7NhOOO5UIyXJJ1zeJMMTjZYQ62gJ4odAsNxL6rr8afW4HwvGtcD0aTjinvEzYh6+N1r2tOlVh6txWjoH3xli86L4rkjKV8vJ0xeL3siJjxsr2UTmmj2gkkhpRspv/3mNAdXe/JHWRiWybFRnHOcXhiEgpSKcuEyJSduwNPGdm5xH6E7cmXLI+M6VRiYhIWtIlChFJOnd/g3DX/mOEbhD3EMZcHZPSwEREJC2py4SIiIiIpDW1EIuIiIhIWlNCLCIiIiJpTQmxiIiIiKQ1JcQiIiIiktaUEIuIiIhIWlNCLCIiIiJp7f8BorwijbbxPGoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\"casual\",\"registered\",\"hum\",\"windspeed\",\"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False# 矩阵上三角设置为0\n",
    "fig,ax= plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "sns.heatmap(corrMatt, mask=mask,vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把 dteday这一列特征进行拆分\n",
    "train['dteday']=pd.to_datetime(train['dteday'])\n",
    "train['day']=train.dteday.apply(lambda x:x.day)\n",
    "train['is_in_first_tendays'] = 0 # 上旬\n",
    "train.loc[train.day.isin(range(1,11)), 'is_in_first_tendays'] = 1\n",
    "train['is_in_middle_tendays'] = 0 # 中旬\n",
    "train.loc[train.day.isin(range(11,21)), 'is_in_middle_tendays'] = 1\n",
    "train['is_in_last_tendays'] = 0 # 下旬\n",
    "train.loc[train.day.isin(range(21,32)), 'is_in_last_tendays'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>day</th>\n",
       "      <th>is_in_first_tendays</th>\n",
       "      <th>is_in_middle_tendays</th>\n",
       "      <th>is_in_last_tendays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant     dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1 2011-01-01       1   0     1        0        6           0   \n",
       "1        2 2011-01-02       1   0     1        0        0           0   \n",
       "2        3 2011-01-03       1   0     1        0        1           1   \n",
       "3        4 2011-01-04       1   0     1        0        2           1   \n",
       "4        5 2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  day  is_in_first_tendays  is_in_middle_tendays  is_in_last_tendays  \n",
       "0   985    1                    1                     0                   0  \n",
       "1   801    2                    1                     0                   0  \n",
       "2  1349    3                    1                     0                   0  \n",
       "3  1562    4                    1                     0                   0  \n",
       "4  1600    5                    1                     0                   0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型、Lasso模型，其中岭回归模型（30分）和Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(584, 16)\n",
      "(147, 16)\n",
      "(584,)\n",
      "(147,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X=train.drop(['casual','registered','cnt','dteday'],axis=1)\n",
    "y=train.cnt\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33, test_size=0.2)\n",
    "# random_state：设定随机种子，确保每次都是按照相同的方式切分数据，以保证最后的结果不会因为数据不一样的因素产生干扰。\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "813.4838664909103"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from  sklearn.metrics import mean_squared_error# 均方误差\n",
    "lr=LinearRegression()# 实例化机器学习模型\n",
    "lr_fit=lr.fit(X_train,y_train)# 用训练数据训练模型参数\n",
    "# 用训练好的模型对测试集进行预测\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_lr)\n",
    "rmse=mse**(1/2)\n",
    "rmse# 用rmse作为评价指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2914817150467951"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对比把y进行log变换之后的结果\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "log_y=np.log1p(y)\n",
    "#std = StandardScaler()\n",
    "#std_y=std.fit_transform(y.values.reshape(-1, 1))# 注意这里要reshape\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,log_y,random_state=33, test_size=0.2)\n",
    "lr_log=LinearRegression()# 实例化机器学习模型\n",
    "lr_fit=lr_log.fit(X_train,y_train)# 用训练数据训练模型参数\n",
    "y_test_pred_lr = lr_log.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_lr)# 对测试集进行预测\n",
    "rmse=mse**(1/2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.06380697e-01,  1.69932832e-01,  3.94020761e+01,  3.22259150e+00,\n",
       "       -1.55192884e-01,  1.53034397e-02,  5.94892638e-02, -2.79505994e-01,\n",
       "        4.97519848e-01,  8.45137576e-01, -1.27347675e-01, -8.69402297e-01,\n",
       "        1.03673864e-01, -1.90075556e-03,  5.14959163e-02, -4.95951607e-02])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_coef=lr_log.coef_\n",
    "lr_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "832.2590957448667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 岭回归,L2正则\n",
    "from sklearn.linear_model import RidgeCV\n",
    "rid=RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1])\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33, test_size=0.2)\n",
    "rid_fit=rid.fit(X_train,y_train)\n",
    "y_train_pred_rid = rid.predict(X_train)\n",
    "y_test_pred_rid = rid.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_rid)# 对测试集进行预测\n",
    "rmse=mse**(1/2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29342038564493006"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对比把y进行log变换之后的结果\n",
    "rid_log=RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1])\n",
    "log_y=np.log1p(y)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,log_y,random_state=33, test_size=0.2)\n",
    "rid_fit=rid_log.fit(X_train,y_train)\n",
    "y_train_pred_rid = rid_log.predict(X_train)\n",
    "y_test_pred_rid = rid_log.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_rid)# 对测试机进行预测\n",
    "rmse=mse**(1/2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.65704450e-02,  1.65737591e-01,  2.11789027e+01,  1.70571623e+00,\n",
       "       -1.63542761e-01,  1.53559228e-02,  5.98952888e-02, -2.78334993e-01,\n",
       "        4.94042251e-01,  9.46598995e-01, -1.29813983e-01, -8.55998416e-01,\n",
       "        5.41410983e-02,  4.52815028e-04,  5.17627153e-02, -5.22155304e-02])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rid_coef=rid_log.coef_\n",
    "rid_coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1453.4244815456184"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso回归，L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "las=LassoCV(cv=5, random_state=0)\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33, test_size=0.2)\n",
    "las_fit=las.fit(X_train,y_train)\n",
    "\n",
    "y_train_pred_las = las.predict(X_train)\n",
    "y_test_pred_las = las.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_las)# 对测试集进行预测\n",
    "rmse=mse**(1/2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.43711271991449235"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对比把y进行log变换之后的结果\n",
    "las_log=LassoCV(cv=5, random_state=0)\n",
    "log_y=np.log1p(y)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,log_y,random_state=33, test_size=0.2)\n",
    "las_fit=las_log.fit(X_train,y_train)\n",
    "\n",
    "y_train_pred_las = las_log.predict(X_train)\n",
    "y_test_pred_las = las_log.predict(X_test)\n",
    "mse=mean_squared_error(y_test,y_test_pred_las)# 对测试集进行预测\n",
    "rmse=mse**(1/2)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00131637,  0.06589737,  0.        , -0.00729754, -0.        ,\n",
       "        0.        ,  0.        , -0.15265755,  0.        ,  0.        ,\n",
       "       -0.        , -0.        , -0.00384665, -0.        ,  0.        ,\n",
       "       -0.        ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_coef=las_log.coef_\n",
    "las_coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分） "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>day</th>\n",
       "      <th>is_in_first_tendays</th>\n",
       "      <th>is_in_middle_tendays</th>\n",
       "      <th>is_in_last_tendays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0        1       1   0     1        0        6           0           2   \n",
       "1        2       1   0     1        0        0           0           2   \n",
       "2        3       1   0     1        0        1           1           1   \n",
       "3        4       1   0     1        0        2           1           1   \n",
       "4        5       1   0     1        0        3           1           1   \n",
       "\n",
       "       temp     atemp       hum  windspeed  day  is_in_first_tendays  \\\n",
       "0  0.344167  0.363625  0.805833   0.160446    1                    1   \n",
       "1  0.363478  0.353739  0.696087   0.248539    2                    1   \n",
       "2  0.196364  0.189405  0.437273   0.248309    3                    1   \n",
       "3  0.200000  0.212122  0.590435   0.160296    4                    1   \n",
       "4  0.226957  0.229270  0.436957   0.186900    5                    1   \n",
       "\n",
       "   is_in_middle_tendays  is_in_last_tendays  \n",
       "0                     0                   0  \n",
       "1                     0                   0  \n",
       "2                     0                   0  \n",
       "3                     0                   0  \n",
       "4                     0                   0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "feat_names=['instant',  'season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday',  'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'day', 'is_in_first_tendays', 'is_in_middle_tendays', 'is_in_last_tendays']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_rid</th>\n",
       "      <th>coef_las</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-0.106381</td>\n",
       "      <td>-0.056570</td>\n",
       "      <td>0.001316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season</td>\n",
       "      <td>0.169933</td>\n",
       "      <td>0.165738</td>\n",
       "      <td>0.065897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yr</td>\n",
       "      <td>39.402076</td>\n",
       "      <td>21.178903</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mnth</td>\n",
       "      <td>3.222592</td>\n",
       "      <td>1.705716</td>\n",
       "      <td>-0.007298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-0.155193</td>\n",
       "      <td>-0.163543</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>weekday</td>\n",
       "      <td>0.015303</td>\n",
       "      <td>0.015356</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>workingday</td>\n",
       "      <td>0.059489</td>\n",
       "      <td>0.059895</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>-0.279506</td>\n",
       "      <td>-0.278335</td>\n",
       "      <td>-0.152658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>temp</td>\n",
       "      <td>0.497520</td>\n",
       "      <td>0.494042</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>atemp</td>\n",
       "      <td>0.845138</td>\n",
       "      <td>0.946599</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hum</td>\n",
       "      <td>-0.127348</td>\n",
       "      <td>-0.129814</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-0.869402</td>\n",
       "      <td>-0.855998</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>day</td>\n",
       "      <td>0.103674</td>\n",
       "      <td>0.054141</td>\n",
       "      <td>-0.003847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>is_in_first_tendays</td>\n",
       "      <td>-0.001901</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>is_in_middle_tendays</td>\n",
       "      <td>0.051496</td>\n",
       "      <td>0.051763</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>is_in_last_tendays</td>\n",
       "      <td>-0.049595</td>\n",
       "      <td>-0.052216</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 columns    coef_lr   coef_rid  coef_las\n",
       "0                instant  -0.106381  -0.056570  0.001316\n",
       "1                 season   0.169933   0.165738  0.065897\n",
       "2                     yr  39.402076  21.178903  0.000000\n",
       "3                   mnth   3.222592   1.705716 -0.007298\n",
       "4                holiday  -0.155193  -0.163543 -0.000000\n",
       "5                weekday   0.015303   0.015356  0.000000\n",
       "6             workingday   0.059489   0.059895  0.000000\n",
       "7             weathersit  -0.279506  -0.278335 -0.152658\n",
       "8                   temp   0.497520   0.494042  0.000000\n",
       "9                  atemp   0.845138   0.946599  0.000000\n",
       "10                   hum  -0.127348  -0.129814 -0.000000\n",
       "11             windspeed  -0.869402  -0.855998 -0.000000\n",
       "12                   day   0.103674   0.054141 -0.003847\n",
       "13   is_in_first_tendays  -0.001901   0.000453 -0.000000\n",
       "14  is_in_middle_tendays   0.051496   0.051763  0.000000\n",
       "15    is_in_last_tendays  -0.049595  -0.052216 -0.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = pd.DataFrame({\"columns\":feat_names, \"coef_lr\":lr_log.coef_,\"coef_rid\":rid_log.coef_,\"coef_las\":las_log.coef_})\n",
    "fs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总结：从上面的结果可以看出线性回归算法在测试集上的表现最好，岭回归和lasso回归都对参数有正则，让参数的浮动范围变小，并且lasso还会起到筛选特征的作用，上面的特征系数很多都是0，但是负面的作用就是利用的特征少了，相应的，得到的结果也不会很好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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